Machine Learning Methods For Prediction Research Articles

Utilization of machine learning methods for prediction of acute and late rectal toxicity due to curative prostate radiotherapy.

Rectal toxicity is one of the primary dose-limiting side effects of prostate cancer radiotherapy, and consequential impairment on quality of life in these patients with long survival is an important problem. In this study, we aimed to evaluate the possibility of predicting rectal toxicity with artificial intelligence model which was including certain dosimetric parameters. One hundred and thirty-seven patients with a diagnosis of prostate cancer who received curative radiotherapy for prostate +/- pelvic lymphatics were included in the study. The association of the clinical data and dosimetric data between early and late rectal toxicity reported during follow-up was evaluated. The sample size was increased to 274 patients by synthetic data generation method. To determine suitable models, 15 models were studied with machine learning algorithms using Python 2.3, Pycaret library. Random forest classifier was used with to detect active variables. The area under the curve and accuracy were found to be 0.89-0.97 and 95%-99%, respectively, with machine learning algorithms. The sensitivity values for acute and toxicity were found to be 0.95 and 0.99, respectively. Early or late rectal toxicity can be predicted with a high probability via dosimetric and physical data and machine learning algorithms of patients who underwent prostate +/- pelvic radiotherapy. The fact that rectal toxicity can be predicted before treatment, which may result in limiting the dose and duration of treatment, makes us think that artificial intelligence can enter our daily practice in a short time in this sense.

Layered production prediction of oil wells based on ConvLSTMA

ABSTRACT With the continuous development of oilfield, oilfield production prediction is becoming more and more important. Most of the current methods are based on machine learning prediction methods and predict the oil production of a single well with relatively low accuracy. In this paper, we focus on dividing single wells into specific layer segments and propose a layered production prediction method for oil wells based on ConvLSTMA model with convolutional neural network, long- and short-term memory network, and fine-grained attention mechanism. The method can effectively utilize stratified historical data to predict future oilfield production and can be adapted to various complex production prediction scenarios. Through experiments, it can be seen that the model performs better than existing models in predicting oil production data, reducing the MSE index by about 2%, MAE index by about 3%, and R2 coefficient by 2%.

Machine learning models for predicting of PD-1 treatment efficacy in Pan-cancer patients based on routine hematologic and biochemical parameters

Immune checkpoint blockade therapy targeting the programmed death-1(PD-1) pathway has shown remarkable efficacy and durable response in patients with various cancer types. Early prediction of therapeutic efficacy is important for optimizing treatment plans and avoiding potential side effects. In this work, we developed an efficient machine learning prediction method using routine hematologic and biochemical parameters to predict the efficacy of PD-1 combination treatment in Pan-Cancer patients. A total of 431 patients with nasopharyngeal carcinoma, esophageal cancer and lung cancer who underwent PD-1 checkpoint inhibitor combination therapy were included in this study. Patients were divided into two groups: progressive disease (PD) and disease control (DC) groups. Hematologic and biochemical parameters were collected before and at the third week of PD-1 therapy. Six machine learning models were developed and trained to predict the efficacy of PD-1 combination therapy at 8–12 weeks. Analysis of 57 blood biomarkers before and after three weeks of PD-1 combination therapy through statistical analysis, heatmaps, and principal component analysis did not accurately predict treatment outcome. However, with machine learning models, both the AdaBoost classifier and GBDT demonstrated high levels of prediction efficiency, with clinically acceptable AUC values exceeding 0.7. The AdaBoost classifier exhibited the highest performance among the 6 machine learning models, with a sensitivity of 0.85 and a specificity of 0.79. Our study demonstrated the potential of machine learning to predict the efficacy of PD-1 combination therapy based on changes in hematologic and biochemical parameters.

Machine learning prediction of the case-fatality of COVID-19 and risk factors for adverse outcomes in patients with non-small cell lung cancer.

Since the emergence of coronavirus disease 2019 (COVID-19) across the globe, patients with cancer have been found to have an increased risk of infection with COVID-19 and are highly likely to experience a severe disease course. This study analyzed the clinical outcomes of COVID-19 in patients with non-small cell lung cancer (NSCLC) and identified the risk factors for adverse outcomes. The study included patients diagnosed with COVID-19 between January 2020 and April 2022 at the Samsung Medical Center in Seoul, Republic of Korea, who have a history of NSCLC. The case-fatality rate and risk factors for COVID-19 were analyzed using a machine-learning prediction method. Additionally, the study investigated the effect of COVID-19 on the systemic treatment of patients with advanced-stage NSCLC. Overall, 1,127 patients were included in this study, with 10.3% of the patients being older than 75 years; of these patients, 51.8% were ex- or current smokers. Among the 584 patients cured after surgery, 91 had stable disease after concurrent chemo-radiotherapy, and 452 had recurrent or metastatic NSCLC. Among 452 patients with recurrent or metastatic NSCLC, 387 received systemic treatment in a palliative setting during COVID-19. Of these, 188 received targeted therapy, 111 received cytotoxic chemotherapy, 63 received immunotherapy +/- chemotherapy, and 26 received other agents. Among them, 94.6% of patients continued systemic treatment after the COVID-19 infection. Only one patient discontinued treatment because of complications of the COVID-19 infection, and 18 patients changed their systemic treatment because of disease progression. The case fatality rates were 0.86% for patients with early-stage NSCLC, 4.4% for patients with locally advanced NSCLC, and 9.96% for patients with advanced NSCLC. The factors associated with fatalities included palliative chemotherapy, type of palliative chemotherapy, age (≥75 years), diabetes, smoking history, history of lung radiotherapy, hypertension, sex, and chronic obstructive pulmonary disease (COPD). The predictive model using logistic regression and eXtreme Gradient Boosting (XGB) performed well [area under the curve (AUC) for logistic regression =0.84 and AUC for XGB =0.84]. The case fatality rate in patients with NSCLC was 4.8%, while most patients with advanced NSCLC continued to receive systemic treatment. However, patients with risk factors require careful management of COVID-19 complications.

Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review

This review focuses on digital soil organic carbon (SOC) mapping at regional or national scales in spatial resolutions up to 1 km using open data remote sensing sources, emphasizing its importance in achieving United Nations’ Sustainable Development Goals (SDGs) related to hunger, climate action, and land conservation. The literature review was performed according to scientific studies indexed in the Web of Science Core Collection database since 2000. The analysis reveals a steady rise in total digital soil mapping studies since 2000, with digital SOC mapping studies accounting for over 20% of these studies in 2023, among which SDGs 2 (Zero Hunger) and 13 (Climate Action) were the most represented. Notably, countries like the United States, China, Germany, and Iran lead in digital SOC mapping research. The shift towards machine and deep learning methods in digital SOC mapping has surged post-2010, necessitating environmental covariates like topography, climate, and spectral data, which are cornerstones of machine and deep learning prediction methods. It was noted that the available climate data primarily restrict the spatial resolution of digital SOC mapping to 1 km, which typically requires downscaling to harmonize with topography (up to 30 m) and multispectral data (up to 10–30 m). Future directions include the integration of diverse remote sensing data sources, the development of advanced algorithms leveraging machine learning, and the utilization of high-resolution remote sensing for more precise SOC mapping.

Comparative analysis of machine learning methods for prediction of chlorophyll-a in a river with different hydrology characteristics: A case study in Fuchun River, China

Eutrophication is a serious threat to water quality and human health, and chlorophyll-a (Chla) is a key indicator to represent eutrophication in rivers or lakes. Understanding the spatial-temporal distribution of Chla and its accurate prediction are significant for water system management. In this study, spatial-temporal analysis and correlation analysis were applied to reveal Chla concentration pattern in the Fuchun River, China. Then four exogenous variables (wind speed, water temperature, dissolved oxygen and turbidity) were used for predicting Chla concentrations by six models (3 traditional machine learning models and 3 deep learning models) and compare the performance in a river with different hydrology characteristics. Statistical analysis shown that the Chla concentration in the reservoir river segment was higher than in the natural river segment during August and September, while the dominant algae gradually changed from Cyanophyta to Cryptophyta. Moreover, air temperature, water temperature and dissolved oxygen had high correlations with Chla concentrations among environment factors. The results of the prediction models demonstrate that extreme gradient boosting (XGBoost) and long short-term memory neural network (LSTM) were the best performance model in the reservoir river segment (NSE = 0.93; RMSE = 4.67) and natural river segment (NSE = 0.94; RMSE = 1.84), respectively. This study provides a reference for further understanding eutrophication and early warning of algal blooms in different type of rivers.

Interpretable machine learning identifies metabolites associated with glomerular filtration rate in type 2 diabetes patients.

The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major public health challenge. Although early detection and intervention can prevent or slow down the progression, the commonly used estimated glomerular filtration rate (eGFR) based on serum creatinine may be influenced by factors unrelated to kidney function. Therefore, there is a need to identify novel biomarkers that can more accurately assess renal function in T2D patients. In this study, we employed an interpretable machine-learning framework to identify plasma metabolomic features associated with GFR in T2D patients. We retrieved 1626 patients with type 2 diabetes (T2D) in Liaoning Medical University First Affiliated Hospital (LMUFAH) as a development cohort and 716 T2D patients in Second Affiliated Hospital of Dalian Medical University (SAHDMU) as an external validation cohort. The metabolite features were screened by the orthogonal partial least squares discriminant analysis (OPLS-DA). We compared machine learning prediction methods, including logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost). The Shapley Additive exPlanations (SHAP) were used to explain the optimal model. For T2D patients, compared with the normal or elevated eGFR group, glutarylcarnitine (C5DC) and decanoylcarnitine (C10) were significantly elevated in GFR mild reduction group, and citrulline and 9 acylcarnitines were also elevated significantly (FDR<0.05, FC > 1.2 and VIP > 1) in moderate or severe reduction group. The XGBoost model with metabolites had the best performance: in the internal validate dataset (AUROC=0.90, AUPRC=0.65, BS=0.064) and external validate cohort (AUROC=0.970, AUPRC=0.857, BS=0.046). Through the SHAP method, we found that C5DC higher than 0.1μmol/L, Cit higher than 26 μmol/L, triglyceride higher than 2 mmol/L, age greater than 65 years old, and duration of T2D more than 10 years were associated with reduced GFR. Elevated plasma levels of citrulline and a panel of acylcarnitines were associated with reduced GFR in T2D patients, independent of other conventional risk factors.

A prediction model for water absorption in sublayers based on stacking ensemble learning method

The water absorption profile can reflect the difference in the waterflooding status of each sublayer in the injection well, and predicting the water absorption profile has important guiding significance for formulating and adjusting the waterflood development plan. Currently, numerical simulation and radioisotope logging techniques are commonly used to obtain the water absorption in sublayers. However, the complex and increasingly heterogeneous subsurface formations in China's oilfields have made it impossible for the numerical simulation to reflect the actual reservoir water injection status by splitting the water absorption into sublayers. Although the radioisotope method is more accurate, it is more expensive and has less actual test data in oilfields. In addition, the water absorption prediction model built by traditional machine learning only applies to injection wells with a small amount of water absorption profile data, and its prediction accuracy needs further improvement. In this paper, a new machine learning prediction method is proposed to build a water absorption profile prediction model by stacking ensemble learning algorithm, which achieves the prediction of water absorption in sublayers for injection wells without historical data of water absorption profiles during the development period. In our model, based on the oilfield's static geological data and dynamic production data, the input feature parameters are comprehensively selected by a hybrid feature selection method to analyze the importance of each feature. The robust base-learners are extracted from the sub-model pool by comparing and analyzing the comprehensive prediction performance of each individual model, including ANN, KNN, SVR, RF, XGBoost, LightGBM, etc. The stacking ensemble model was trained using K-fold cross-validation on a randomly divided dataset, which improves the predictive performance of an individual model by combining the advantages of multiple base-learners so that it can accurately reflect the dynamic changes of the water absorption profile. Finally, a case study from the MG study area of the DG oilfield was conducted to verify the model's predictive effect. This case shows that the stacking model proposed in this paper has better performance in predicting the water absorption in sublayers compared with the traditional single model and other ensemble models, and the model has a strong generalization ability, which proves that the method can be used in studying the prediction of the water absorption in sublayers of injection wells.

Machine Learning Methods for Prediction of Biomedical Properties of Nanomaterials

Machine Learning Methods for Prediction of Biomedical Properties of Nanomaterials

Prediction methods for oral diseases

With the increasing advancement of science and technology, especially the rapid development of electronic computer technology, machine learning methods have obvious advantages in predicting oral diseases. Especially in the research on oral cancer, this article uses a variety of machine learning methods for prediction and has achieved some remarkable results to provide a basis for subsequent disease treatment. In the prediction analysis of oral cancer, logistic regression, Cox regression, and machine learning algorithms were used to make a preliminary judgment on the triggering factors of oral cancer. The main triggers were dietary habits, betel nut chewing, smoking, and excessive drinking. Secondly, this article explores solutions to oral cancer prediction problems. By comparing various methods, it is concluded that Cox regression is generally used to predict patient survival or recurrence rate, while logistic regression and machine learning algorithms are used to induce Judgment and evaluation of factors, among which machine learning algorithms mainly identify factors with a wide range and high complexity, while logistic regression is used to identify some specific factors. Oral cancer is a malignant tumor. Therefore, early prediction and treatment of oral cancer are not only crucial to the patient's survival rate and oral quality but also play a major role in the progress of medicine and the development of human society.

Prediction of Grain Porosity Based on WOA–BPNN and Grain Compression Experiment

The multi-field coupling of grain piles in grain silos is a focal point of research in the field of grain storage. The porosity of grain piles is a critical parameter that affects the heat and moisture transfer in grain piles. To investigate the distribution law of the bulk grain pile porosity in grain silos, machine learning algorithms were incorporated into the prediction model for grain porosity. Firstly, this study acquired the database by conducting compression experiments on grain specimens and collecting data from the literature. The back propagation neural network (BPNN) algorithm was optimized using three metaheuristic algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA)). Five machine learning models (GA–BPNN, PSO–BPNN, WOA–BPNN, BPNN, and random forest (RF)) were developed to predict the grain porosity using three input parameters (vertical pressure, grain type, and moisture content). The five models were assessed using four evaluation metrics: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), to determine the best porosity prediction model. Finally, the generalization ability of the best prediction model was verified using the results of the grain cell box experiment on wheat piles. The results indicated that the WOA–BPNN model was the best prediction model with an R2 value of 0.9542, an RMSE value of 0.0079, an MAE value of 0.0044, and an MAPE value of 1.1467%. The WOA–BPNN model demonstrated strong generalization ability, confirming the feasibility of using this model to predict grain porosity. It also established an expression for the relationship between wheat porosity and the vertical pressure of the grain pile. This study presents a machine learning prediction method for determining the porosity of grain piles. The obtained porosity distribution law serves as a crucial basis for conducting comprehensive multi-field coupling analysis of grain piles and offers theoretical support for safe grain storage.

Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review.

In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions in predicting residual stresses in laser additive manufacturing.

Designing and modeling of a new MSMA vibration energy transducer

The conversion of mechanical vibrational energy into electrical energy to power wireless electronic devices using the smart material magnetic shape memory alloy (MSMA) has garnered substantial attention. This paper presents a vibration energy transducer founded on the inverse effect of MSMA, elucidating the principle of power generation. The variations in martensite and magnetic domain characteristics within MSMA were analyzed, and a constitutive model was established for the MSMA vibration energy transducer, integrating the thermodynamic theory of the Gibbs free energy function. Although this model performs well in predicting experimental outcomes, it falls short in capturing all features of the experimental data. To comprehensively encompass these features, the PSO-XGBoost machine learning approach was introduced to train the experimental data by incorporating factors such as stress, magnetic field, and induced voltage. An experimental prototype of the MSMA vibration energy transducer is fabricated, and the predictions of both models are compared with the collected experimental data, validating the accuracy of the model and indicating the enhanced effectiveness of machine learning methods in prediction. This research not only validates the correctness of the models but also emphasizes the potential for more precise predictions using machine learning methods, thereby establishing a robust foundation for the thorough study and broader application of MSMA vibration energy transducers.

The application of machine learning methods for prediction of heavy metal by activated carbons, biochars, and carbon nanotubes

Carbonaceous materials are commonly used as adsorbents for heavy metals. The determination of the adsorption capacity needs time and energy, and the key factors affecting the adsorption capacity have not been determined. Therefore, a new and efficient method is needed to predict the adsorption capacity and explore the decisive factors in the adsorption process. In this study, three tree-based machine learning models (i.e., random forest, gradient boosting decision tree, and extreme gradient boosting) were developed to predict the adsorption capacity of eight heavy metals (i.e., As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) on activated carbons, biochars, and carbon nanotubes using 3674 data points extracted from 151 journal articles. After a comprehensive comparison, the gradient boosting decision tree had the best performance for a combined model based on all data (R2 = 0.9707, RMSE = 0.1420). Moreover, independent models were developed for three datasets classified by the adsorbent and eight datasets classified by the heavy metals. In addition, a graphical user interface was built to predict the adsorption capacity of heavy metals. This study provides a novel strategy and convenient tool for the removal of heavy metals and can help to improve the removal efficiency of heavy metals to build a healthier world.

A fusion gas load prediction model with three-way residual error amendment

Accurately predicting gas load is crucial for optimal planning and scheduling of natural gas production. Existing machine learning or deep learning-based prediction methods primarily aim to improve accuracy. However, these methods face challenges such as overlapped features, significant background noise, and residual errors. In response to these challenges, we proposed a fusion prediction model, known as the Double Temporal Convolutional Network (TCN) and Light Gradient Boosting Machine (LightGBM) with Three-way Residual Error Amendment (DT-LGBM-3WREA). To address the overlapped feature issue in time series data, we utilize the STL module for data decomposition. To handle background noise, the network incorporates a double TCN module specifically designed for trend and seasonal components. Thus, it can better avoid the transmission of noise between modules. To address residual error, we introduce three-way decision methodology and develop the 3WREA module, which aims to avoid local optima and random factors from perturbing the prediction accuracy. This approach mitigates the influence of remainder item on overall prediction performance. Experimental results demonstrate that the DT-LGBM-3WREA model excels in gas load prediction scenarios, surpassing state-of-the-art methods.

A joint multi-model machine learning prediction approach based on confidence for ship stability

Since the traditional ship stability failure probability assessment method has many input parameters and cumbersome intermediate calculation process, this paper proposes a joint multi-model machine learning prediction method based on confidence. The method calculates the confidence of each machine learning model for the current prediction result, selects the top n models among them, and takes the average of their prediction results as the output. The confidence is calculated by a method inspired by semi-supervised learning. To reduce the number of ship features that need to be input for assessment, a sensitivity analysis is used to reject irrelevant features. Eight machine learning models that have good performance and are widely used in other fields, including Radial Basis Function Neural Network, Random Forest, eXtreme Gradient Boosting, and so on, are used as component learners in this study. The hyperparameters of each model are obtained by cross-validation and grid search. The advancedness of the proposed method is verified by comparing it with other models on a small self-built ship stability failure probability dataset. By conducting experiments that simply average the results of the component learners, it is confirmed that simple superposition different models does not necessarily improve the accuracy. At the same time, after pre-processing the input features in different ways, the comparison of the prediction performance was conducted, and the experimental results showed that the proposed method is not affected by the way the input features are preprocessed and therefore has some robustness.

An initial investigation into the use of machine learning methods for prediction of carcass component yields in F2 broiler chickens

Context As evaluation of carcass components is costly and time consuming, models for prediction of broiler carcass components are useful. Aims The aim was to investigate the feasibility of machine learning methods in the prediction of carcass components from measurements on live birds during the rearing period. Methods Three machine learning methods, including regression tree, random forest and gradient-boosting trees, were applied to predict carcass yields, and benchmarked against classical linear regression. Two scenarios were defined for prediction. In the first scenario, carcass yields were predicted by live bodyweight, shank length and shank diameter features, recorded at 2, 3 and 4 weeks of age. In the second scenario, predictor features recorded at 5, 6 and 7 weeks of age were used. The two scenarios were reanalysed by including effective single-nucleotide polymorphisms associated with bodyweight, shank length and shank diameter as new predictor features. Key results The correlation coefficient between predicted and observed values for predicting weight of carcass traits ranged from 0.50 for wing to 0.59 for thigh in the first scenario, and from 0.63 for wing to 0.74 for carcass in the second scenario. These predictions for the percentage of carcass components ranged from 0.30 for wing to 0.39 for carcass and breast in the first scenario, and from 0.34 for thigh to 0.43 for carcass in the second scenario when random forest was used. Conclusions Predictive accuracy in the first scenario was lower than in the second scenario for all prediction methods. Including single-nucleotide polymorphisms as predictor features in either scenario did not increase the accuracy of the prediction. Implications In general, random forest had the best performance among machine learning methods, and classical linear regression in two scenarios, suggesting that it may be considered as an alternative to conventional linear models for prediction of carcass traits in broiler chickens.

FRP-XGBoost: Identification of ferroptosis-related proteins based on multi-view features

Ferroptosis represents a novel form of programmed cell death. Pan-cancer bioinformatics analysis indicates that identifying and modulating ferroptosis offer innovative approaches for preventing and treating diverse tumor pathologies. However, the precise detection of ferroptosis-related proteins via conventional wet-laboratory techniques remains a formidable challenge, largely due to the constraints of existing methodologies. These traditional approaches are not only labor-intensive but also financially burdensome. Consequently, there is an imperative need for the development of more sophisticated and efficient computational tools to facilitate the detection of these proteins. In this paper, we presented a XGBoost and multi-view features-based machine learning prediction method for predicting ferroptosis-related proteins, which was referred to as FRP-XGBoost. In this study, we explored four types of protein feature extraction methods and evaluated their effectiveness in predicting ferroptosis-related proteins using six of the most commonly used traditional classifiers. To enhance the representational power of the hybrid features, we employed a two-step feature selection technique to identify the optimal subset of features. Subsequently, we constructed a prediction model using the XGBoost algorithm. The FRP-XGBoost achieved an accuracy of 96.74 % in 10-fold cross-validation and a further accuracy of 91.52 % in an independent test. The implementation source code of FRP-XGBoost is available at https://github.com/linli5417/FRP-XGBoost.

Comparison of different machine learning methods for prediction of ultrasonic pulse velocity

Deneysel sonuçlardan elde edilen basınç dayanımı sonuçlarına bağlı olarak ultrases dalgası hızı sonuçlarının tahmin edilmesi amacıyla, farklı oranlarda mineral katkı içeren on iki (12) farklı çimento harcı üretilmiştir. Üretilen harç numunelerinin 1, 3, 7, 28 ve 90 günlük kür yaşları için hem basınç dayanımı hem de ultrases dalgası hızı sonuçları deneysel olarak elde edilmiştir. Farklı kür koşulları için harç numunelerinden elde edilen basınç dayanımı deneysel verileri Aşırı Öğrenme Makinesi, Destek Vektör Makinesi ve Grup Veri İşleme Yöntemi olmak üzere üç farklı regresyon yöntemi kullanılarak ultrases dalgası hızı değerlerinin tahmininde kullanılmıştır. Regresyon yöntemlerinin uygulanmasında iki farklı yaklaşım izlenmiştir. İlk yaklaşımda, farklı kür yaşları için ultrases dalgası hızı sonuçları, basınç dayanımı değerleri göz ardı edilerek tahmin edilmiştir. Diğer yaklaşımda ise ultrases dalgası hızı sonuçlarını tahmin etmek için basınç dayanımı değerleri dikkate alınmıştır. Böylece hem basınç dayanımı hem de ultrases dalgası hızı değerleri ile regresyon modelleri ile elde edilen başarı sonuçları arasındaki ilişki belirlenip, karşılaştırılmıştır. Bu çalışmada, Yöntem 1'deki Grup Veri İşleme Yöntemi modeli ile en iyi test performansları (yani R2 ve MSE için) sırasıyla 0.856 ve 0.037; Yöntem 2'de ise Grup Veri İşleme Yöntemi modeli ile en iyi test performansları sırasıyla 0.977 ve 0.003 olarak bulunmuştur. Elde edilen sonuçlar dikkate alındığında, ultrases dalgası hızı değerleri seçilen regresyon modelleri ile yüksek başarı oranlarıyla elde edilmiştir.

Machine learning based biomarker discovery for chronic kidney disease-mineral and bone disorder (CKD-MBD).

Chronic kidney disease-mineral and bone disorder (CKD-MBD) is characterized by bone abnormalities, vascular calcification, and some other complications. Although there are diagnostic criteria for CKD-MBD, in situations when conducting target feature examining are unavailable, there is a need to investigate and discover alternative biochemical criteria that are easy to obtain. Moreover, studying the correlations between the newly discovered biomarkers and the existing ones may provide insights into the underlying molecular mechanisms of CKD-MBD. We collected a cohort of 116 individuals, consisting of three subtypes of CKD-MBD: calcium abnormality, phosphorus abnormality, and PTH abnormality. To identify the best biomarker panel for discrimination, we conducted six machine learning prediction methods and employed a sequential forward feature selection approach for each subtype. Additionally, we collected a separate prospective cohort of 114 samples to validate the discriminative power of the trained prediction models. Using machine learning under cross validation setting, the feature selection method selected a concise biomarker panel for each CKD-MBD subtype as well as for the general one. Using the consensus of these features, best area under ROC curve reached up to 0.95 for the training dataset and 0.74 for the perspective dataset, respectively. For the first time, we utilized machine learning methods to analyze biochemical criteria associated with CKD-MBD. Our aim was to identify alternative biomarkers that could serve not only as early detection indicators for CKD-MBD, but also as potential candidates for studying the underlying molecular mechanisms of the condition.