Evaluate Machine Learning Algorithms Research Articles

Improved patient mortality predictions in emergency departments with deep learning data-synthesis and ensemble models

The triage process in emergency departments (EDs) relies on the subjective assessment of medical practitioners, making it unreliable in certain aspects. There is a need for a more accurate and objective algorithm to determine the urgency of patients. This paper explores the application of advanced data-synthesis algorithms, machine learning (ML) algorithms, and ensemble models to predict patient mortality. Patients predicted to be at risk of mortality are in a highly critical condition, signifying an urgent need for immediate medical intervention. This paper aims to determine the most effective method for predicting mortality by enhancing the F1 score while maintaining high area under the receiver operating characteristic curve (AUC) score. This study used a dataset of 7325 patients who visited the Yonsei Severance Hospital’s ED, located in Seoul, South Korea. The patients were divided into two groups: patients who deceased in the ED and patients who didn’t. Various data-synthesis techniques, such as SMOTE, ADASYN, CTGAN, TVAE, CopulaGAN, and Gaussian Copula, were deployed to generate synthetic patient data. Twenty two ML models were then utilized, including tree-based algorithms like Decision tree, AdaBoost, LightGBM, CatBoost, XGBoost, NGBoost, TabNet, which are deep neural network algorithms, and statistical algorithms such as Support Vector Machine, Logistic Regression, Random Forest, k-nearest neighbors, and Gaussian Naive Bayes, as well as Ensemble Models which use the results from the ML models. Based on 21 patient information features used in the pandemic influenza triage algorithm (PITA), the models explained previously were applied to aim for the prediction of patient mortality. In evaluating ML algorithms using an imbalanced medical dataset, conventional metrics like accuracy scores or AUC can be misleading. This paper emphasizes the importance of using the F1 score as the primary performance measure, focusing on recall and specificity in detecting patient mortality. The highest-ranked model for predicting mortality utilized the Gaussian Copula data-synthesis technique and the CatBoost classifier, achieving an AUC of 0.9731 and an F1 score of 0.7059. These findings highlight the effectiveness of machine learning algorithms and data-synthesis techniques in improving the prediction performance of mortality in EDs.

Churn Identification and Prediction from a Large-Scale Telecommunication Dataset Using NLP

The identification of customer churn is a major issue for large telecom businesses. In order to manage the data of current customers as well as acquire and manage new customers, every day, a substantial volume of data gets generated. Therefore, it's crucial to identify the causes of client churn so that the appropriate steps can be taken to lower it. Numerous researchers have already discussed their efforts to combine static and dynamic approaches in order to reduce churn in big data sets, but these systems still have many issues when it comes to actually identifying churn. In this paper, we suggested two methods, the first of which is churn identification and using Natural Language Processing (NLP) methods and machine learning techniques, we make predictions based on a vast telecommunication data set. The NLP process involves data pre-processing, normalization, feature extraction, and feature selection. For feature extraction, we employ unique techniques like TF-IDF, Stanford NLP, and occurrence correlation methods, have been suggested. Throughout the lesson, a machine learning classification algorithm is used for training and testing. Finally, the system employs a variety of cross validation techniques and training and evaluating Machine learning algorithms. The experimental analysis shows the system's efficacy and accuracy.

Prediction of combustion, performance, and emission parameters of ethanol powered spark ignition engine using ensemble Least Squares boosting machine learning algorithms

This research concentrates on the application of machine learning techniques to predict combustion, performance, and emission parameters in a dual-fuel spark ignition (SI) engine powered by neat gasoline and E20 ethanol dual fuel. The goal is to overcome the limitations posed by repeated engine experiments and nonlinear test results. In order to optimise engine parameters, the research seeks to develop efficient machine learning models with high generalizability and employ an optimisation strategy to determine the optimal engine settings. Input for training and evaluating machine learning algorithms, such as Artificial Neural Networks (ANN), and Ensemble LS Boosting was derived from experimental data from a combustion test engine, which includes Neat gasoline, and Ethanol dual fuel blend E20 at various load conditions. The dataset includes engine combustion, performance, and emission indices such as brake thermal efficiency (BTE), Exhaust Gas Temperature (EGT), Hydrocarbons (HC), Carbon monoxide (CO), Carbon dioxide (CO2), and Nitrogen oxides (NOx), under various operating conditions. Load and brake-specific fuel consumption (BSFC) were training input attributes. Using a comprehensive experimental database of input-output engine parameters, the Artificial Neural Network (ANN) and Ensemble LS Boosting were constructed. The training data points were resampled to generate multiple training datasets for training different models. 50 test samples were used to evaluate the generalisation capability of the machine learning models, while BTE, EGT, CO, CO2, HC and NOx, were the primary parameters subject to prediction. The optimal machine learning method was determined by comparing R-squared (R2) values, root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Using multiple hyperparameter tuning iterations, the agreement between actual and predicted values for diverse Ensemble LS Boost algorithms was evaluated. The Ensemble LS Boost model exhibits the maximum level of agreement between predicted and experimental engine parameters across all datasets when compared to the other ANN models. This finding was corroborated by additional research based on test datasets, specifically the test sample interpolation data, which measures generalisation ability. The study also focuses on developing and applying two unique, interactive Simulink models for the Spark Ignition (SI) engine that are tailored for Neat Gasoline and Ethanol E20 test fuels under all loads. The key component of the model-based development technique in MATLAB and Simulink was the incorporation of sophisticated machine learning algorithms, i.e., Ensemble Least-Squares (LS) Boosting, to the model-based development workflow which produced reliable results. Implementing an Ensemble LS Boost machine learning framework is therefore highly recommended as an efficient method for predicting and optimising the combustion, performance, and emission characteristics of dual-fuel gasoline engines utilising Ethanol-based dual-fuel blends.

FracAtlas: A Dataset for Fracture Classification, Localization and Segmentation of Musculoskeletal Radiographs

Digital radiography is one of the most common and cost-effective standards for the diagnosis of bone fractures. For such diagnoses expert intervention is required which is time-consuming and demands rigorous training. With the recent growth of computer vision algorithms, there is a surge of interest in computer-aided diagnosis. The development of algorithms demands large datasets with proper annotations. Existing X-Ray datasets are either small or lack proper annotation, which hinders the development of machine-learning algorithms and evaluation of the relative performance of algorithms for classification, localization, and segmentation. We present FracAtlas, a new dataset of X-Ray scans curated from the images collected from 3 major hospitals in Bangladesh. Our dataset includes 4,083 images that have been manually annotated for bone fracture classification, localization, and segmentation with the help of 2 expert radiologists and an orthopedist using the open-source labeling platform, makesense.ai. There are 717 images with 922 instances of fractures. Each of the fracture instances has its own mask and bounding box, whereas the scans also have global labels for classification tasks. We believe the dataset will be a valuable resource for researchers interested in developing and evaluating machine learning algorithms for bone fracture diagnosis.

Evaluating machine learning algorithms to Predict 30-day Unplanned REadmission (PURE) in Urology patients

BackgroundUnplanned hospital readmissions are serious medical adverse events, stressful to patients, and expensive for hospitals. This study aims to develop a probability calculator to predict unplanned readmissions (PURE) within 30-days after discharge from the department of Urology, and evaluate the respective diagnostic performance characteristics of the PURE probability calculator developed with machine learning (ML) algorithms comparing regression versus classification algorithms.MethodsEight ML models (i.e. logistic regression, LASSO regression, RIDGE regression, decision tree, bagged trees, boosted trees, XGBoost trees, RandomForest) were trained on 5.323 unique patients with 52 different features, and evaluated on diagnostic performance of PURE within 30 days of discharge from the department of Urology.ResultsOur main findings were that performances from classification to regression algorithms had good AUC scores (0.62–0.82), and classification algorithms showed a stronger overall performance as compared to models trained with regression algorithms. Tuning the best model, XGBoost, resulted in an accuracy of 0.83, sensitivity of 0.86, specificity of 0.57, AUC of 0.81, PPV of 0.95, and a NPV of 0.31.ConclusionsClassification models showed stronger performance than regression models with reliable prediction for patients with high probability of readmission, and should be considered as first choice. The tuned XGBoost model shows performance that indicates safe clinical appliance for discharge management in order to prevent an unplanned readmission at the department of Urology.

Machine learning algorithms and computational validation of single-nucleotide polymorphisms of antioxidant enzymes and oxidative stress markers in neonates.

Aim: To evaluate machine learning algorithms (MLAs) for predicting factors (oxidative stress biomarkers [OSBs] and single-nucleotide polymorphism of the antioxidant enzymes)for respiratory distress syndrome (RDS) and significant alterations in the liver functions (SALVs). Materials & methods: MLAs were applied for predicting the RDS and SALV (with OSB and single-nucleotide polymorphisms in the antioxidant enzymes) with area under the curve(AUC)as the accuracy measure. Results: The C5.0 algorithm best predicted SALV (AUC: 0.63) with catalase as the most important predictor. Bayesian network best predicted RDS (AUC: 0.6) andENOS1 was the most important predictor. Conclusion: MLAs carry great potential in identifying the potential genetic and OSBsin neonatal RDS and SALV. Validation in prospective studies is needed urgently.

REVIEW PAPER ON PREDICTION OF WATER QUALITY PARAMETER USING MACHINE LEARNING

Surface water pollution become a nuisance for humankind as river water fulfill requirement of a major population and traditional method of water quality assessment and evaluation is inadequate in this era. So using advance method of machine learning in prediction of surface water proves to be helpful to prevent future water accident. As we seen many recent studies of water quality prediction and river water assessment using machine learning approach for better accuracy and less labor and to optimize its overall results. It’s become essential to review the recent studies which used Machine Learning algorithms for prediction, analysis, evaluation and assessment of river water quality and different models used in these studies for different environmental conditions. Machine learning models are superior to handle such complex and non linear data such as water quality parameters with greater accuracy, reliability, cost-effectiveness and efficiency as considered as great tool for surface Water Quality monitoring, prediction, future projects and help lawmakers in policy. In this report we reviewed around 17 research papers which uses machine learning approach from different journal and concise it to covers the structure of study, datasets used, methodology analysis, models performance, environment susceptibility, comparative analysis and assessments of Machine Learning models progress in river water quality research. For better management and control of surface water quality and its treatment, this study will help in understanding and analyzing the studies reviewed in this paper and its future application. We can conclude that research on Water Quality prediction using Machine Learning model are inadequate in the context future vulnerability, observing increasing pollution in recent years we require more research in this field. Finally, this study provides breakthrough in Surface Water Engineering and Management to give a new direction to fore coming studies and fortified it scope also gives a comparative approach for its implementation in new studies.

Predicting Pain in People With Sickle Cell Disease in the Day Hospital Using the Commercial Wearable Apple Watch: Feasibility Study.

Sickle cell disease (SCD) is a genetic red blood cell disorder associated with severe complications including chronic anemia, stroke, and vaso-occlusive crises (VOCs). VOCs are unpredictable, difficult to treat, and the leading cause of hospitalization. Recent efforts have focused on the use of mobile health technology to develop algorithms to predict pain in people with sickle cell disease. Combining the data collection abilities of a consumer wearable, such as the Apple Watch, and machine learning techniques may help us better understand the pain experience and find trends to predict pain from VOCs. The aim of this study is to (1) determine the feasibility of using the Apple Watch to predict the pain scores in people with sickle cell disease admitted to the Duke University SCD Day Hospital, referred to as the Day Hospital, and (2) build and evaluate machine learning algorithms to predict the pain scores of VOCs with the Apple Watch. Following approval of the institutional review board, patients with sickle cell disease, older than 18 years, and admitted to Day Hospital for a VOC between July 2021 and September 2021 were approached to participate in the study. Participants were provided with an Apple Watch Series 3, which is to be worn for the duration of their visit. Data collected from the Apple Watch included heart rate, heart rate variability (calculated), and calories. Pain scores and vital signs were collected from the electronic medical record. Data were analyzed using 3 different machine learning models: multinomial logistic regression, gradient boosting, and random forest, and 2 null models, to assess the accuracy of pain scores. The evaluation metrics considered were accuracy (F1-score), area under the receiving operating characteristic curve, and root-mean-square error (RMSE). We enrolled 20 patients with sickle cell disease, all of whom identified as Black or African American and consisted of 12 (60%) females and 8 (40%) males. There were 14 individuals diagnosed with hemoglobin type SS (70%). The median age of the population was 35.5 (IQR 30-41) years. The median time each individual spent wearing the Apple Watch was 2 hours and 17 minutes and a total of 15,683 data points were collected across the population. All models outperformed the null models, and the best-performing model was the random forest model, which was able to predict the pain scores with an accuracy of 84.5%, and a RMSE of 0.84. The strong performance of the model in all metrics validates feasibility and the ability to use data collected from a noninvasive device, the Apple Watch, to predict the pain scores during VOCs. It is a novel and feasible approach and presents a low-cost method that could benefit clinicians and individuals with sickle cell disease in the treatment of VOCs.

Cerebrospinal fluid and serum proteomic profiles accurately distinguish neuroaxonal dystrophy from cervical vertebral compressive myelopathy in horses

Cervical vertebral compressive myelopathy (CVCM) and equine neuroaxonal dystrophy/degenerative myeloencephalopathy (eNAD/EDM) are leading causes of spinal ataxia in horses. The conditions can be difficult to differentiate, and there is currently no diagnostic modality that offers a definitive antemortem diagnosis. Evaluate novel proteomic techniques and machine learning algorithms to predict biomarkers that can aid in the antemortem diagnosis of noninfectious spinal ataxia in horses. Banked serum and cerebrospinal fluid (CSF) samples from necropsy-confirmed adult eNAD/EDM (n = 47) and CVCM (n = 25) horses and neurologically normal adult horses (n = 45). . A subset of serum and CSF samples from eNAD/EDM (n=5) and normal (n=5) horses was used to evaluate the proximity extension assay (PEA). All samples were assayed by PEA for 368 neurologically relevant proteins. Data were analyzed using machine learning algorithms to define potential diagnostic biomarkers. Of the 368 proteins, 84 were detected in CSF and 146 in serum. Eighteen of 84 proteins in CSF and 30/146 in serum were differentially abundant among the 3 groups, after correction for multiple testing. Modeling indicated that a 2-protein test using CSF had the highest accuracy for discriminating among all 3 groups. Cerebrospinal fluid R-spondin 1 (RSPO1) and neurofilament-light (NEFL), in parallel, predicted normal horses with an accuracy of 87.18%, CVCM with 84.62%, and eNAD/EDM with 73.5%. Cross-species platform. Uneven sample size. Proximity extension assay technology allows for rapid screening of equine biologic matrices for potential protein biomarkers. Machine learning analysis allows for unbiased selection of highly accurate biomarkers from high-dimensional data.

Machine learning-based colorectal cancer prediction using global dietary data

BackgroundColorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Active health screening for CRC yielded detection of an increasingly younger adults. However, current machine learning algorithms that are trained using older adults and smaller datasets, may not perform well in practice for large populations.AimTo evaluate machine learning algorithms using large datasets accounting for both younger and older adults from multiple regions and diverse sociodemographics.MethodsA large dataset including 109,343 participants in a dietary-based colorectal cancer ase study from Canada, India, Italy, South Korea, Mexico, Sweden, and the United States was collected by the Center for Disease Control and Prevention. This global dietary database was augmented with other publicly accessible information from multiple sources. Nine supervised and unsupervised machine learning algorithms were evaluated on the aggregated dataset.ResultsBoth supervised and unsupervised models performed well in predicting CRC and non-CRC phenotypes. A prediction model based on an artificial neural network (ANN) was found to be the optimal algorithm with CRC misclassification of 1% and non-CRC misclassification of 3%.ConclusionsANN models trained on large heterogeneous datasets may be applicable for both younger and older adults. Such models provide a solid foundation for building effective clinical decision support systems assisting healthcare providers in dietary-related, non-invasive screening that can be applied in large studies. Using optimal algorithms coupled with high compliance to cancer screening is expected to significantly improve early diagnoses and boost the success rate of timely and appropriate cancer interventions.

Predicting Global Ranking of Universities Across the World Using Machine Learning Regression Technique

Digital transformation in the field of education plays a significant role especially when used for analysis of various teaching and learning parameters to predict global ranking index of the universities across the world. Machine learning is a subset of computer science facilitates machine to learn the data using various algorithms and predict the results. This research explores the Quacquarelli Symonds approach for evaluating global university rankings and develop machine learning models for predicting global rankings. The research uses exploratory data analysis for analysing the dataset and then evaluate machine learning algorithms using regression techniques for predicting the global rankings. The research also addresses the future scope towards evaluating machine learning algorithms for predicting outcomes using classification and clustering techniques.

A method for the identification of denial of service (DoS) attack in network traffic using machine learning techniques

Computer Networks and the internet are essential to our daily lives and enterprises. DoS assaults threaten computer networks and network security. The world is evolving toward online businesses and services. This has increased network traffic over time. We need NIDS and DoS attack detection since there are more network risks and attacks. DoS attacks now threaten computer network servers. This threat must be detected automatically to protect corporate assets. Anomaly-based intrusion detection was developed because signature-based DoS attack and intrusion detection methods are inadequate. Many studies employ Machine Learning and Deep Learning to detect network anamolies. This article describes classification models constructed with the aid of machine learning algorithms. On the own dataset, this research was performed utilizing machine learning algorithms including K-Nearest Neighbor (KNN), Logistic Regression, and Random Forest. Random Forest outperforms other supervised machine learning algorithms, as demonstrated by this study's findings. It achieved an accuracy rate of 99.62% when nine features were selected utilizing Pearson's correlation coefficient method. The own dataset file (myNetworkGenerateTraffic.csv) which was captured through wireshark tool were utilized to accurately evaluate machine learning algorithms. We utilized the following performance metrics in this investigation: Accuracy, Precision, Recall, and F-1 score. In this paper, we examine how machine learning techniques can improve DoS attack prediction in network traffic to better analyze network traffic and help improve network security.

Evaluating machine learning algorithms to classify forest tree species through satellite imagery

Recent events indicate an uptick in forest fires in the western United States, prompting cities and organizations to develop a better understanding of forests and how to manage them. Tree species classification is important for forest management and carbon sequestration analysis. Currently, remote sensing stands as the prevalent method for the classification of tree species, land cover, etc. Researchers often use machine learning techniques for classification and general remote sensing. We hypothesized that it is possible to classify forest tree species with high classification accuracy using solely RGB values as the inputs for the machine learning models. We experimented with different machine learning algorithms such as Random Forest (RF), k-Nearest Neighbors (kNN), Gradient Boosting (GB), and Linear Discriminant Analysis (LDA) to classify forest tree species, specifically through multispectral Landsat 8 satellite imagery. Each algorithm was trained and validated using the same dataset and satellite imagery of the same region. Our findings indicated RF had the highest classification accuracy of 95.4% for validation on the same general region it trained on. kNN, GB, and LDA had classification accuracies of 81.6%, 76.4%, and 64.6%, respectively. Based on these results, we concluded that RF is the more accurate algorithm for classifying tree species through RGB satellite imagery. Our findings also indicate that model training and inference on the same general region result in higher classification accuracy. However, as the inference region changes, the classification accuracy reduces. In such cases, additional predictor variables, including trunk diameter, crown shape, and vegetation indices, could be introduced to improve classification accuracy.

ADABase: A Multimodal Dataset for Cognitive Load Estimation.

Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on physiological measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as behavioral measurements such as action units extracted from facial videos, performance metrics like reaction time and subjective feedback using questionnaires, we create ADABase (Autonomous Driving Cognitive Load Assessment Database) As a reference method to induce cognitive load onto subjects, we use the well-established n-back test, in addition to our novel simulator-based k-drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.

Philosophical Inquiry into Computer Intentionality: Machine Learning and Value Sensitive Design

Abstract Intelligent algorithms together with various machine learning techniques hold a dominant position among major challenges for contemporary value sensitive design. Self-learning capabilities of current AI applications blur the causal link between programmer and computer behavior. This creates a vital challenge for the design, development and implementation of digital technologies nowadays. This paper seeks to provide an account of this challenge. The main question that shapes the current analysis is the following: What conceptual tools can be developed within the value sensitive design school of thought for evaluating machine learning algorithms where the causal relation between designers and the behavior of their computer systems has been eroded? The answer to this question will be provided through two levels of investigation within the value sensitive design methodology. The first level is conceptual. Within the conceptual level, we will introduce the notion of computer intentionality and will show how this term may be used for solving an issue of non-causal relation between designer and computer system. The second level of investigation is technical. At this level the emphasis will be given to machine learning algorithms.

Digital mapping of coffee ripeness using UAV-based multispectral imagery

Timely and accurate monitoring of coffee ripeness is essential for harvest planning, especially in mountainous areas where the harvest is performed manually due to the limited use of agricultural mechanization. The increasing temporal and spatial resolutions of remote sensing based on low-altitude unmanned aerial vehicles (UAV) provides a feasible way to monitor the fruit ripeness variability. Due to these facts, this study was aimed to: (1) predict the fruit ripeness using spectral and textural variables; and (2) to determine the best variables for developing spatio-temporal variability maps of the fruit ripeness. To do so, an experiment with six arabica coffee fields was set up. During the coffee ripeness stage in the 2018–2019 and 2020–2021 seasons, seven flights were carried out using a quadcopter equipped with a five-band multispectral camera. After that, 12 spectral and 64 textural variables composed of bands and vegetation indices were obtained. For the same period, the percentage of unripe fruits (fruit ripeness) was determined using an irregular grid on each field. Then, the fruit ripeness was predicted with six machine learning (ML) algorithms using as input (1) the spectral variables and (2) the combination of spectral and textural variables. Among the evaluated ML algorithms, the random forest presented the higher accuracy, in which the model using the spectral and textural variables (r2 = 0.71 and RMSE = 11.47%) presented superior performance than the model based solely on spectral variables (r2 = 0.67 and RMSE = 12.09%). Finally, this study demonstrated the feasibility of using spectral and textural variables derived from UAV imagery for mapping and monitoring the spatiotemporal changes in the fruit ripeness at a fine scale.

Machine learning based outcome prediction of large vessel occlusion of the anterior circulation prior to thrombectomy in patients with wake-up stroke.

Outcome prediction of large vessel occlusion of the anterior circulation in patients with wake-up stroke is important to identify patients that will benefit from thrombectomy. Currently, mismatch concepts that require MRI or CT-Perfusion (CTP) are recommended to identify these patients. We evaluated machine learning algorithms in their ability to discriminate between patients with favorable (defined as a modified Rankin Scale (mRS) score of 0-2) and unfavorable (mRS 3-6) outcome and between patients with poor (mRS5-6) and non-poor (mRS 0-4) outcome. Data of 8395 patients that were treated between 2018 and 2020 from the nationwide registry of the German Society for Neuroradiology was retrospectively analyzed. Five models were trained with clinical variables and Alberta Stroke Program Early CT Score (ASPECTS). The model with the highest accuracy was validated with a test dataset with known stroke onset and with a test dataset that consisted only of wake-up strokes. 2419 patients showed poor and 3310 patients showed favorable outcome. The best performing Random Forest model achieved a sensitivity of 0.65, a specificity of 0.81 and an AUC of 0.79 on the test dataset of patients with wake-up stroke in the classification analysis between favorable and unfavorable outcome and a sensitivity of 0.42, a specificity of 0.83 and an AUC of 0.72 in the classification analysis between poor and non-poor outcome. Machine learning algorithms have the potential to aid in the decision making for thrombectomy in patients with wake-up stroke especially in hospitals, where emergency CTP or MRI imaging is not available.

Development of a Human Motion Analysis System Based on Sensorized Insoles and Machine Learning Algorithms for Gait Evaluation

Human gait analysis can provide an excellent source for identifying and predicting pathologies and injuries. In this respect, sensorized insoles also have a great potential for extracting gait information. This, combined with mathematical techniques based on machine learning (ML), can potentialize biomechanical analyses. The present study proposes a proof-of-concept of a system based on vertical ground reaction force (vGRF) acquisition with a sensorized insole that uses an ML algorithm to identify different patterns of vGRF and extract biomechanical characteristics that can help during clinical evaluation. The acquired data from the system was clustered by an immunological algorithm (IA) based on vGRF during gait. These clusters underwent a data mining process using the classification and regression tree algorithm (CART), where the main characteristics of each group were extracted, and some rules for gait classification were created. As a result, the system proposed was able to collect and process the biomechanical behavior of gait. After the application of IA and CART algorithms, six groups were found. The characteristics of each of these groups were extracted and verified the capability of the system to collect and process the biomechanical behavior of gait, offering verification points that can help focus during a clinical evaluation.

Plasma Proteomics Enable Differentiation of Lung Adenocarcinoma from Chronic Obstructive Pulmonary Disease (COPD).

Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel.

Kayıp IoT Verilerinin Makine Öğrenmesi Teknikleri ile Tahmini

Every day, the amount of data generated by industrial applications based on the Internet of Things (IoT) grows. However, data acquired as a result of failures and communication disconnections in IoT devices might be noisy, inaccurate, and incomplete. These issues have become crucial for data production, quality, processing, and analysis. The datasets used in the scope of this study were collected in real-time from the water neutralizer system of Sivas Numune Hospital, which converts medical waste into household waste. Medical liquid wastes in hospitals are exposed to chemical neutralization process by means of pH change with neutralization devices before being transferred to the sewer. In this regard, the monitoring of pH levels in the medical waste neutralization system is crucial for environmental protection. In this aspect, two datasets with varying quantities of missing data were evaluated for the prediction of the PH using the linear regression (LR), support vector machines (SVM), k-nearest neighbor (KNN), random forest (RF), and decision tree (DT) machine learning algorithms. Mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) performance metrics were used to evaluate machine learning algorithms. As a consequence of the analysis, it was determined that the SVM algorithm performed better performance on the two distinct datasets. The result of the evaluation indicates that machine learning algorithms are remarkably efficient at predicting missing pH data.