Accuracy Of Machine Learning Models Research Articles

The usefulness of machine learning analysis for predicting the presence of depression with the results of the Korea National Health and Nutrition Examination Survey.

Depression is a major public health concern, with an estimated 10.8% of adults experiencing depression. Depression can have a significant impact on an individual's quality of life, social function, and productivity. Early diagnosis of depression is important in preventing its progression. Several tools, such as the Patient Health Questionnaire-9 (PHQ-9) and Beck Depression Inventory, are used to screen patients for depression. We investigated the potential of machine learning in predicting the presence of depression using the results of a national survey. We collected the data of 5,420 patients from the 2020 Korea National Health and Nutrition Examination. The presence of depression was defined as ≥5 PHQ-9. We categorized output variables into the presence of depression (PHQ-9, ≥5) and absence of depression (PHQ-9, <5). We used 20 variables related to sociodemographic characteristics, health behavior, and presence of chronic disease for the development of three machine learning algorithms [random forest, logistic regression, and deep neural network (DNN)]. Eighty-seven decision trees were used for the random forest model. Linear regression algorithm shows a linear relationship between various input and output variables. For the DNN model, three layers with 16-32-64 neurons, Adam optimizer, and rectified linear unit (ReLU) activation were used. Of the included samples, 70% and 30% were randomly divided into the training and test sets, respectively. The area under the curve (AUC) of the test dataset for the random forest model was 0.803 [95% confidence interval (CI), 0.776-0.829], 0.812 (95% CI, 0.787-0.837) for the logistic regression model, and 0.805 (95% CI, 0.780-0.831) for the DNN model. Our study demonstrated the potential of machine learning for the development of models for predicting the presence of depression based of various health-related data. Machine learning models can potentially overcome the limitations of traditional diagnostic methods for depression by incorporating a wide range of objective variables to accurately identify patients with depression, thus avoiding the subjectivity and potential diagnostic errors associated with the subjective interpretation of symptoms observed by a clinician. Further efforts to increase the accuracy of machine learning models by utilizing more variables and data needed to detect depression.

FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction.

Graph Convolutional Neural Networks (GCNs) are widely used for graph analysis. Specifically, in medical applications, GCNs can be used for disease prediction on a population graph, where graph nodes represent individuals and edges represent individual similarities. However, GCNs rely on a vast amount of data, which is challenging to collect for a single medical institution. In addition, a critical challenge that most medical institutions continue to face is addressing disease prediction in isolation with incomplete data information. To address these issues, Federated Learning (FL) allows isolated local institutions to collaboratively train a global model without data sharing. In this work, we propose a framework, FedNI, to leverage network inpainting and inter-institutional data via FL. Specifically, we first federatively train missing node and edge predictor using a graph generative adversarial network (GAN) to complete the missing information of local networks. Then we train a global GCN node classifier across institutions using a federated graph learning platform. The novel design enables us to build more accurate machine learning models by leveraging federated learning and also graph learning approaches. We demonstrate that our federated model outperforms local and baseline FL methods with significant margins on two public neuroimaging datasets.

APPLICATION OF MACHINE LEARNING ALGORITMS FOR HEART DISEASE PREDICTION

This paper focuses on the application of machine learning algorithms to predict cardiovascular diseases (CVDs). Every year a large number of deaths are registered all over the world. According to the World Health Organisation, CVDs are the leading cause of high mortality in the world. One of the necessary preventive measures to reduce mortality from CVDs is the timely prediction of diseases in people at high risk of such diseases. Specially developed scales and machine learning algorithms are now being used for the timely prediction of CVDs.&#x0D; Background. To predict heart disease, algorithms are often used: naive Bayesian classifier (Gaussian Naïve Bayes Classificator, GNBC), k-nearest neighbours (K-Nearest Neghboors, KNN), decision tree (Decision Tree, DT). In domestic literature, there are known works devoted to the application of SWD prediction using Adam gradient algorithm in deep neural network training. One of the necessary conditions for increasing the predictive ability of a machine learning model (MLM) is the optimal selection of hyperparameters. The choice of the optimal hyperparameters is often made on the basis of empirical experience.&#x0D; Purpose. To explore the specific application of machine algorithms to the prediction of heart disease.&#x0D; Materials and methods. Scientific novelty of the work. In this research we analyse machine learning algorithms for predicting the risk of CVDs using the approach of automatic search for hyperparameters MMO. The following algorithms were used to construct MMOs: NBS, KNN, DT, Logistic Regression, Support Vector Machine (SVM), Random Foorest (RF), Complement Naïve Bayes Classificator (CNBC), Linear Discriminant Analysis (LDA), Radial Basic Function (RBF), Gradient Boost (XGBoost). To evaluate the accuracy of machine learning models we used the following indicators: mean absolute error (MAE), precision, completeness (recall), F-measure (F-beta), False Positive Rate (FPR), False Negative Rate (FNR). Additionally, visual analysis of ROC curve (receiver operating characteristic) and areas under the curve (areas under the curve, AUC) were used to analyse the results of MMO. Using AUC value allows to estimate prognostic ability of MLM.&#x0D; Results. The training results showed that RF and XGBoost algorithms are characterized by higher accuracy. With optimal selection of MMO parameters, the overall classification accuracy was 0.88 and 0.94 respectively.&#x0D; Conclusion. The application of machine learning algorithms allows predictive models to be built with high accuracy. This requires the construction of a machine learning model. The ensemble machine learning algorithms RF and XGBoost have higher accuracy rates than the following algorithms: decision trees, Bayesian classification methods, logistic regression, linear discriminant analysis.

Software Defect Prediction Based on Optimized Machine Learning Models: A Comparative Study

Software defect prediction is crucial used for detecting possible defects in software before they manifest. While machine learning models have become more prevalent in software defect prediction, their effectiveness may vary based on the dataset and hyperparameters of the model. Difficulties arise in determining the most suitable hyperparameters for the model, as well as identifying the prominent features that serve as input to the classifier. This research aims to evaluate various traditional machine learning models that are optimized for software defect prediction on NASA MDP (Metrics Data Program) datasets. The datasets were classified using k-nearest neighbors (k-NN), decision trees, logistic regression, linear discriminant analysis (LDA), single hidden layer multilayer perceptron (SHL-MLP), and Support Vector Machine (SVM). The hyperparameters of the models were fine-tuned using random search, and the feature dimensionality was decreased by utilizing principal component analysis (PCA). The synthetic minority oversampling technique (SMOTE) was implemented to oversample the minority class in order to correct the class imbalance. k-NN was found to be the most suitable for software defect prediction on several datasets, while SHL-MLP and SVM were also effective on certain datasets. It is noteworthy that logistic regression and LDA did not perform as well as the other models. Moreover, the optimized models outperform the baseline models in terms of classification accuracy. The choice of model for software defect prediction should be based on the specific characteristics of the dataset. Furthermore, hyperparameter tuning can improve the accuracy of machine learning models in predicting software defects.

DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical record.

Heatlhcare institutions are establishing frameworks to govern and promote the implementation of accurate, actionable, and reliable machine learning models that integrate with clinical workflow. Such governance frameworks require an accompanying technical framework to deploy models in a resource efficient, safe and high-quality manner. Here we present DEPLOYR, a technical framework for enabling real-time deployment and monitoring of researcher-created models into a widely used electronic medical record system. We discuss core functionality and design decisions, including mechanisms to trigger inference based on actions within electronic medical record software, modules that collect real-time data to make inferences, mechanisms that close-the-loop by displaying inferences back to end-users within their workflow, monitoring modules that track performance of deployed models over time, silent deployment capabilities, and mechanisms to prospectively evaluate a deployed model's impact. We demonstrate the use of DEPLOYR by silently deploying and prospectively evaluating 12 machine learning models trained using electronic medical record data that predict laboratory diagnostic results, triggered by clinician button-clicks in Stanford Health Care's electronic medical record. Our study highlights the need and feasibility for such silent deployment, because prospectively measured performance varies from retrospective estimates. When possible, we recommend using prospectively estimated performance measures during silent trials to make final go decisions for model deployment. Machine learning applications in healthcare are extensively researched, but successful translations to the bedside are rare. By describing DEPLOYR, we aim to inform machine learning deployment best practices and help bridge the model implementation gap.

Prediction of tensile performance for 3D printed photopolymer gyroid lattices using structural porosity, base material properties, and machine learning

Advancements in additive manufacturing (AM) technology and three-dimensional (3D) modeling software have enabled the fabrication of parts with combinations of properties that were impossible to achieve with traditional manufacturing techniques. Porous designs such as truss-based and sheet-based lattices have gained much attention in recent years due to their versatility. The multitude of lattice design possibilities, coupled with a growing list of available 3D printing materials, has provided a vast range of 3D printable structures that can be used to achieve desired performance. However, the process of computationally or experimentally evaluating many combinations of base material and lattice design for a given application is impractical. This research proposes a framework for quickly predicting key mechanical properties of 3D printed gyroid lattices using information about the base material and porosity of the structure. Experimental data was gathered to train a simple, interpretable, and accurate kernel ridge regression machine learning model. The performance of the model was then compared to numerical simulation data and demonstrated similar accuracy at a fraction of the computation time. Ultimately, the model development serves as an advancement in ML-driven mechanical property prediction that can be used to guide extension of current and future models.

Developing high-dimensional machine learning models to improve generalization ability and overcome data insufficiency for mixed sugar fermentation simulation

Biorefinery can be promoted by building accurate machine learning models. This work proposed a strategy to enhance model’s generalization ability and overcome insufficient data conditions for mixed sugar fermentation simulation. Multiple inputs single output models, using initial glucose, initial xylose, and time together as inputs, have higher generalization ability than single input single output models with time as sole input in predicting glucose, xylose, ethanol, or biomass separately. Multiple inputs multiple outputs models, integrating outputs, enhanced model accuracy and resulted in an average R2 at 0.99. To overcome data insufficiency conditions, consensus yeast (CY) model, through consolidating data from 4 yeasts, obtained R2 at 0.90. By adjusting the pretrained CY model, the model can save more than 50% data and get R2 at 0.95 and 0.93 for yeast and bacterial fermentation simulation. The strategy can expand the application range and save costs of data curation for ANN models.

Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer

Objective:Lung cancer exhibits unpredictable recurrence in low-stage tumors and variable responses to different therapeutic interventions. Predicting relapse in early-stage lung cancer can facilitate precision medicine and improve patient survivability. While existing machine learning models rely on clinical data, incorporating genomic information could enhance their efficiency. This study aims to impute and integrate specific types of genomic data with clinical data to improve the accuracy of machine learning models for predicting relapse in early-stage, non-small cell lung cancer patients. Methods:The study utilized a publicly available TCGA lung cancer cohort and imputed genetic pathway scores into the Spanish Lung Cancer Group (SLCG) data, specifically in 1348 early-stage patients. Initially, tumor recurrence was predicted without imputed pathway scores. Subsequently, the SLCG data were augmented with pathway scores imputed from TCGA. The integrative approach aimed to enhance relapse risk prediction performance. Results:The integrative approach achieved improved relapse risk prediction with the following evaluation metrics: an area under the precision–recall curve (PR-AUC) score of 0.75, an area under the ROC (ROC-AUC) score of 0.80, an F1 score of 0.61, and a Precision of 0.80. The prediction explanation model SHAP (SHapley Additive exPlanations) was employed to explain the machine learning model’s predictions. Conclusion:We conclude that our explainable predictive model is a promising tool for oncologists that addresses an unmet clinical need of post-treatment patient stratification based on the relapse risk while also improving the predictive power by incorporating proxy genomic data not available for specific patients.

Machine learning of redundant energy of a solar PV Mini-grid system for cooking applications

Solar PV mini-grids are increasingly being deployed in off-grid and island communities especially in sub-Saharan Africa (SSA) countries to meet household energy demand. However, one challenge of solar PV mini-grids for community energy supply is the mismatch between the PV energy generation and household energy demand. PV mini-grid energy generation is highest in the afternoon whilst household energy demand is highest in the mornings and evenings, but lowest in the afternoons. This mismatch creates redundant energy generation during peak sunshine hours when battery energy storage is full, leading to low profitability for mini-grid systems. In this study, four machine learning models have been applied on an installed 30.6 kW mini-grid system in Ghana to ascertain the level of the redundant energy. The study has revealed that redundant energy exists on the mini-grid, in the range of 56.98 – 119.86 kWh/day. Further analysis has shown that the redundant energy can support household cooking energy demand through sustainable thermal batteries. With the four machine learning (ML) models applied in predicting the redundant energy, the most accurate ML model, K-nearest Neighbour Regressor, had a root mean square error (RMSE) of 0.148 and a coefficient of determination (R2) value of 0.998.

POPNASv3: A pareto-optimal neural architecture search solution for image and time series classification

The growing demand for machine learning applications in industry has created a need for fast and efficient methods to develop accurate machine learning models. Automated Machine Learning (AutoML) algorithms have emerged as a promising solution to this problem, designing models without the need for human expertise. Given the effectiveness of neural network models, Neural Architecture Search (NAS) specialises in designing their architectures autonomously, with results that rival the most advanced hand-crafted models. However, this approach requires significant computational resources and hardware investment, making it less attractive for real-world applications. This article presents the third version of Pareto-Optimal Progressive Neural Architecture Search (POPNASv3), a new NAS algorithm that employs Sequential Model-Based Optimisation and Pareto optimality. This choice makes POPNASv3 flexible to different hardware environments, computational budgets and tasks, as the algorithm can efficiently explore user-defined search spaces of varying complexity. Pareto optimality extracts the architectures that achieve the best trade-off with respect to the metrics considered, reducing the number of models sampled during the search and dramatically improving time efficiency without sacrificing accuracy. The experiments performed on image and time series classification datasets provide evidence that POPNASv3 can explore a large set of different operators and converge to optimal architectures suited to the type of data provided under different scenarios.11The algorithm is open source and available on Zenodo at https://zenodo.org/record/7439022. The repository contains the code, the URIs of the datasets used in the experiments, and the models stored in ONNX format.

Performance comparison of landslide susceptibility mapping under multiple machine-learning based models considering InSAR deformation: a case study of the upper Jinsha River

Landslide susceptibility mapping (LSM) comprehensively evaluates the spatial probability of landslide occurrence by using different environmental factors. However, most of the evaluation methods ignore the dynamic characteristic factors of landslides, which makes it difficult to obtain reliable prediction results. Taking the upper reaches of the Jinsha River as the study area, this article introduces the deformation data into the landslide characteristic model and proposes an improved landslide susceptibility evaluation method. Four kinds of landslide susceptibility machine learning models were constructed by collecting 20 landslide related factors. The prediction accuracy of machine learning models is compared, and the performance of different models and the improvement of model performance by deformation information are evaluated. The results show that the performance of Random Forest and XGBoost model is better than SVM and logistic regression model. The prediction accuracy of Random Forest and XGBoost model is improved obviously after InSAR deformation is introduced. 96.9 and 93.19% of landslide areas were reasonably classified as high or very high risk levels. Compared with the calculation result of traditional model, the proportion of high and very high risk pixels in landslide area is increased by 2.97 and 1.13%, respectively. In addition, the percentage of high and very high risk areas in the susceptibility evaluation area increased from 15.45 to 16.23% and 18.73 to 21.89%, respectively. The accuracy of Random Forest and XGBoost models increased from 0.793 to 0.878 and 0.776 to 0.812, respectively, and the AUC increased by 0.9 and 1.7%, respectively. The SHAP and traditional feature importance analysis reveals that rainfall, aspect, temperature and NDVI are the main influencing factors of landslide in the upper reaches of the Jinsha River.

Comparative study on the performance of different machine learning techniques to predict the shear strength of RC deep beams: Model selection and industry implications

This study presents a comprehensive and rigorous process to develop the most appropriate machine learning (ML) model for predicting the shear strength of RC deep beams (RCDBs). The process consists of the crucial stages and state-of-the-art techniques of ML, including the development of ML models, selection of input features using Shapley Additive explanations, optimisation of the training process, assessment of data randomness, comparisons to the conventional practice codes, and development of novel web-based design platform based on the proposed ML model. For this purpose, seven machine learning models, i.e., linear regression, artificial neural networks (ANN), support vector machines, decision trees, ensemble of trees (EoT), extreme gradient boosting (XGBoost), and Gaussian process regression (GPR) were developed to predict the shear strength of RC deep beams based on a database of 518 samples with 15 input features. The four best models (i.e., ANN, EoT, XGBoost, and GPR) were then considered to assess the influence of varying the number of input features on the prediction performance. The results proved that GPR is the most reliable and accurate ML model. In addition, a set of nine optimal input features is proposed for predicting the shear strength of RCDBs. It was observed that randomly dividing the dataset into training and testing sets can significantly impact the predicted results. In some cases, the R2 value dropped to under 0.78, highlighting the importance of carefully considering the methodology for dividing the dataset when conducting machine learning experiments. The shear strength predicted by ML models was then compared with the three most prominent practice codes (i.e., ACI318, EC2, CSA 23.3-04), which indicated ML approach is highly reliable and accurate over conventional methods. In addition, the study used the Monte Carlo method to evaluate the robustness of the machine learning models and developed a user-interface platform to facilitate the practical application of the proposed machine learning model.

Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance

Landslides pose a serious threat to human lives and property. Accurate landslide susceptibility mapping (LSM) is crucial for sustainable development. Machine learning has recently become an important means of LSM. However, the accuracy of machine learning models is limited by the heterogeneity of environmental factors and the imbalance of samples, especially for large-scale LSM. To address these problems, we created an improved random forest (RF)-based LSM model and applied it to Guangdong Province, China. First, the RF-based LSM model was constructed using rainfall-induced landslide samples and 13 environmental factors and by exploring the optimal positive-to-negative and training-to-test sample ratios. Second, the performance of the RF-based LSM model was evaluated and compared with three other machine learning models. The results indicate that: (1) the proposed RF-based model has the best performance with the highest area under curve (AUC) of 0.9145, based on optimal positive-to-negative and training-to-test sample ratios of 1:1 and 8:2, respectively; (2) the introduction of rainfall and global human modification (GHM) can increase the AUC from 0.8808 to 0.9145; and (3) rainfall and topography are two dominant factors in Guangdong landslides. These findings can facilitate landslide risk prevention and serve as a technical reference for large-scale accurate LSM.

PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections

We present PSO-PARSIMONY, a new methodology to search for parsimonious and highly accurate models by means of particle swarm optimization. PSO-PARSIMONY uses automatic hyperparameter optimization and feature selection to search for accurate models with low complexity. To evaluate the new proposal, a comparative study with multilayer perceptron algorithm was performed with public datasets and by applying it to predict two important parameters of the force–displacement curve in T-stub steel connections: initial stiffness and maximum strength. Models optimized with PSO-PARSIMONY showed an excellent trade-off between goodness-of-fit and parsimony. The new proposal was compared with GA-PARSIMONY, our previously published methodology that uses genetic algorithms in the optimization process. The new method needed more iterations and obtained slightly more complex individuals, but it performed better in the search for accurate models.

Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP

Nasopharyngeal cancer (NPC) has a unique histopathology compared with other head and neck cancers. Individual NPC patients may attain different outcomes. This study aims to build a prognostic system by combining a highly accurate machine learning model (ML) model with explainable artificial intelligence to stratify NPC patients into low and high chance of survival groups. Explainability is provided using Local Interpretable Model Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) techniques. A total of 1094 NPC patients were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database for model training and internal validation. We combined five different ML algorithms to form a uniquely stacked algorithm. The predictive performance of the stacked algorithm was compared with a state-of-the-art algorithm—extreme gradient boosting (XGBoost) to stratify the NPC patients into chance of survival groups. We validated our model with temporal validation (n = 547) and geographic external validation (Helsinki University Hospital NPC cohort, n = 60). The developed stacked predictive ML model showed an accuracy of 85.9% while the XGBoost had 84.5% after the training and testing phases. This demonstrated that both XGBoost and the stacked model showed comparable performance. External geographic validation of XGBoost model showed a c-index of 0.74, accuracy of 76.7%, and area under curve of 0.76. The SHAP technique revealed that age of the patient at diagnosis, T-stage, ethnicity, M-stage, marital status, and grade were among the prominent input variables in decreasing order of significance for the overall survival of NPC patients. LIME showed the degree of reliability of the prediction made by the model. In addition, both techniques showed how each feature contributed to the prediction made by the model. LIME and SHAP techniques provided personalized protective and risk factors for each NPC patient and unraveled some novel non-linear relationships between input features and survival chance. The examined ML approach showed the ability to predict the chance of overall survival of NPC patients. This is important for effective treatment planning care and informed clinical decisions. To enhance outcome results, including survival in NPC, ML may aid in planning individualized therapy for this patient population.

Decoding the gene subnetworks prioritized in prediction with GLRP in Alzheimer’s Disease

AbstractBackgroundAlzheimer’s disease is the leading cause of brain dementia, along with which substantial failure of organs and mental issues arise. The abundance of AD related data in the current decade has allowed for much advancement in the field using modern machine learning and deep learning techniques to decrypt pathology. Though diagnostic tools have been modeled over such large expanses of data, the black box problem of decoding the significant biological components contributing to the prediction has been overlooked in the research field.MethodGLRP is a method to decode the final predictions of a deep learning method. Since Graph‐CNN are best suited for using the gene expression data modeled over an established whole human PPI network. The usage of GLRP allows us to build the relevance that we need to interpret how the genes contribute to the prediction based on proven PPI network topology as the basis. From the relevance’s obtained of correctly predicted ROSMAP AD subjects and controls, important genes were extracted to be analyzed for functional annotations and pathway analysis with HPRD PPI.ResultThe graph‐CNN model was able to predict between AD subjects and controls with an AUC of 76.58% and test accuracy of 79.167% while the highest accuracy of machine learning models capped at 75% with an AUC of 72.95% from SVM as seen in Table 1. Having found a threshold of 550 genes to have the highest connected component in graph before noisy genes are added to the network as seen in Fig 1, the average relevance of AD subjects and controls provided us with 214 common genes with 181 genes in the largest connected component as seen in Fig 2.ConclusionAlong with tackling the black box problem of the deep learning model using GLRP which has allowed us to gather the important genes from the network view at a higher efficiency than the machine learning models. The top 214 common genes and the network relating to the negative regulation of organelle organization, hypoxia and oxygen level response, and cellular response to tumor necrosis factors could have potential impact on the pathology of AD and its progression.

Prediction of concrete compressive strength employing machine learning techniques

Machine learning (ML) methods have proven to be reliable techniques in estimating, classifying, and predicting material strength based on varying material properties. It is observed that the nature of the problem and the data at hand governs the selection of an appropriate ML method. Therefore, to determine the accuracy of ML models to predict concrete compressive strength, fifteen different ML algorithms were applied to a given concrete compressive strength dataset. Among all the ML algorithms used, the prediction accuracy of the Support Vector Regressor (SVR) was the highest (88.18%) due to its effective performance when dealing with continuous target variables and nonlinear relationships among the features and the target.

MLOps for Enhancing the Accuracy of Machine Learning Models using DevOps, Continuous Integration, and Continuous Deployment

Machine learning (ML) integrated with development and operations (DevOps) is the key to solving the problem of deploying the latest machine learning models. This paper proposes one of the ways of integrating machine learning with DevOps. The need for this integration is endless as this provides seamless upgradation of the so-created models while also making managing and monitoring simple. The paper also provides light on practices of Continuous Integration/Continuous Deployment (CI/CD) and minimizing the unnecessary loss of time while training an ML model. The procedure followed includes CI/CD that contains jobs to train the models and to roll out the model with maximum performance. The main focus of this paper is the dynamic change of hyperparameters to achieve increased accuracy without the necessity of the physical presence of humans to change it. This research is independent of the type of machine learning model used and can be best followed for neural networks.

Biomarker-driven prospective clinical trial in renal cell carcinoma: Developing machine learning models to allocate patients to treatment arms using RNA sequencing.

4525 Background: RNA sequencing has shown promise in defining the biology of individual renal cell carcinoma (RCC) tumors. However, these biomarkers have not yet been translated to the clinic for prospectively assigning optimal treatments to patients. Challenges for RNA-seq biomarker development include translating classifiers across different assays/platforms, normalization of data collected from single patients in the clinic and establishing robust thresholds for assigning prediction groups. We have designed the prospective phase II OPTIC RCC clinical trial (clinicaltrials.gov NCT05361720) to test the utility of an RNA-seq based biomarker in predicting treatment based on biologic drivers relevant to angiogenesis (anti-angiogenesis; TKI) and immune microenvironment (immunotherapy; IO). Here, we will describe the development and optimization of a machine learning model for assigning individual patients to biologically driven clusters in real time to facilitate RNA-seq based biomarker trials. Methods: We have utilized RNA-seq data from the IMmotion 151 trial (ref) to develop a machine learning model. Clusters were grouped into three based on their association to tumor biology and treatment: (1) Cluster 1+2 (angiogenic signature → TKI+IO therapy), (2) Cluster 4+5 (immune/proliferative signature → dual IO therapy), (3) Cluster 3+6 (neither signature → exclude). Random forest classifier was used to train a multi-class model to predict the three groups. The model is evaluated using bootstrapped cross-validation. Results: Our machine learning classifier was built using 188 genes and has a cross-validation accuracy of 85% and sensitivity of &gt;90% in predicting patients into one of the three biological clusters from our training data. Predictions of the classifier are significantly associated with progression-free survival across different treatments within each of the predicted groups. We also observed significant odds ratios when comparing responders (CR/PR/SD) to non-responders (PD) across the treatment groups. The model was then validated in two independent test sets treated with Angio and IO inhibitors: (1) 61 patient renal cell carcinoma cohort (2) 12 patient clear cell renal cell carcinoma cohort. We observed a significant enrichment of responders to Angio+IO treatment for the predicted Cluster_Angio patients compared to IO treatment (p=0.05), highlighting the importance of matching patients to their optimal treatment. Conclusions: We have developed an accurate machine learning model to assign individual patients to RNA-seq clusters in real time. This classifier will facilitate the prospective OPTIC RCC trial. If successful, our biomarker strategy will serve as a proof of concept for selecting optimal treatments for RCC patients.

Detection and prediction of monkey pox disease by enhanced convolutional neural network approach

Monkeypox is an infectious viral disease affecting both humans and animals. All symptoms are accompanied by a fever, swollen lymph nodes, and a rash that blisters before crusting. The interval between exposure and the development of symptoms is 5 to 21 days. Typically, symptoms last between two and four weeks. Although it is unknown to what degree it can happen without any signs. It has been found that not all outbreaks display the typical symptoms of fever, aches in the muscles, enlarged glands, and lesions appearing simultaneously. Cases may be severe, especially in children, pregnant women, or people with compromised immune systems. The problem can be detected and monitored at the early stages using some engineering solutions. Therefore, there is a necessity to develop accurate machine learning models for accurate interpretation before applying them in clinical trials. Hence, the proposed work has developed a model to diagnose monkey pox at the best accurate levels for accurate interpretation. The proposed enhanced convolutional neural network model is compared with the exisiting approaches. The obtained results were compared and indicate the superiority of the proposed algorithm.