Soil Nutrient Analysis and Yield Prediction With Neuro‐ ML Ensemble Model Using IoT ‐ WSN Approach: In Context to India's Agricultural Sector

Predicting mortality among trauma patients is a critical task that can guide clinical decision-making, better management and resource allocation in Intensive Care Units (ICU). Machine learning has been increasingly employed in clinical practice to effectively predict the mortality of critically ill patients. This study aimed to develop and evaluate the machine learning models for predicting the mortality of trauma patients. A multicentre cross-sectional study was conducted. The data were collected retrospectively from 613 trauma patients admitted to the ICU between January 1, 2020, and December 30, 2021, in comprehensive specialised hospitals of Northwest Ethiopia. The Kampala trauma score (KTS II) and revised trauma score (RTS) were calculated for each patient on admission, and the scores range from 5 to 10 and 0 to 7.84, respectively, with lower scores indicating more severe trauma and a higher risk of mortality. Pre-processing, feature selection and model fitting were done using Python version 3.12. Seven Machine Learning (ML) models, Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbours (KNN), Logistic Regression (LR), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), were developed to predict mortality among trauma patients at the time of hospital discharge. The dataset was divided into training (80%) and testing sets (20%), and a 10-fold cross-validation technique was employed to improve model performance. The models' prediction accuracy was measured using metrics derived from the confusion matrix, such as sensitivity, specificity, precision and Receiver Operating Characteristics (ROC). Of 613 trauma patients admitted to the intensive care units, 248 (40.5%) died. Among the different variables included, the Kampala trauma score (KTS II), Glasgow Coma Scale (GCS) score and presence of complications were the most reliable features for predicting mortality among trauma patients. This study found that the Random Forest (RF) algorithm outperformed other machine learning algorithms, achieving an accuracy of 95%, sensitivity of 96%, precision of 93%, F1 score of 94% and a Receiver Operating Characteristics (ROC) score of 99%. Moreover, Support Vector Machines (SVM) and XGBoost also performed exceptionally well, with AUC scores of 0.98 and 0.97, respectively. We found the Random Forest (RF) to be the best-performing machine learning model in predicting the mortality among trauma patients. This is the first machine learning model developed specifically for mortality prediction of trauma patients in Ethiopia. The application of machine learning algorithms is warranted to stratify the risk of mortality, enabling evidence-based intervention and maximising resource utilisation. Thus, further external validation on independent data from prospective studies is needed to evaluate the universal applicability of the model to clinical practice. This study holds substantial value for clinical practice by enhancing decision support, enabling early identification of high-risk patients and supporting proactive surveillance and timely interventions. The integration of machine learning for mortality prediction is particularly impactful, as it facilitates remote monitoring and telemedicine, helping to bridge gaps in healthcare access. Additionally, it aids in optimising treatment strategies through patient-centred data, informs health planning and resource allocation, supports personalised care and advances data-driven research and policy-making.

PurposeUsing machine learning method to predict and judge unknown data offers opportunity to improve accuracy by exploring complex interactions between risk factors. Therefore, we evaluate the performance of machine learning (ML) algorithms and to compare them with logistic regression for predicting the risk of renal function decline (RFD) using routine clinical data.Patients and MethodsThis retrospective cohort study includes datasets from 2166 subjects, aged 35–74 years old, provided by an adult health screening follow-up program between 2010 and 2020. Seven different ML models were considered – random forest, gradient boosting, multilayer perceptron, support vector machine, K-nearest neighbors, adaptive boosting, and decision tree - and were compared with standard logistic regression. There were 24 independent variables, and the baseline estimate glomerular filtration rate (eGFR) was used as the predictive variable.ResultsA total of 2166 participants (mean age 49.2±11.2 years old, 63.3% males) were enrolled and randomly divided into a training set (n=1732) and a test set (n=434). The area under receiver operating characteristic curve (AUROC) for detecting RFD corresponding to the different models were above 0.85 during the training phase. The gradient boosting algorithms exhibited the best average prediction accuracy (AUROC: 0.914) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the RFD prediction performance, compared to logistic regression model (AUROC:0.882), except the K-nearest neighbors and decision tree algorithms (AUROC:0.854 and 0.824, respectively). However, the improvement differences with logistic regression were small (less than 4%) and nonsignificant.ConclusionOur results indicate that the proposed health screening dataset-based RFD prediction model using ML algorithms is readily applicable, produces validated results. But logistic regression yields as good performance as ML models to predict the risk of RFD with simple clinical predictors.

Myocardial perfusion imaging (MPI) is an essential tool used to diagnose and manage patients with suspected or known coronary artery disease. Additionally, the General Data Protection Regulation (GDPR) represents a milestone about individuals' data security concerns. On the other hand, Machine Learning (ML) has had several applications in the most diverse knowledge areas. It is conceived as a technology with huge potential to revolutionize health care. In this context, we developed ML models to evaluate their ability to distinguish an individual's sex from MPI assessment. We used 260 polar maps (140 men/120 women) to train ML algorithms from a database of patients referred to a university hospital for clinically indicated MPI from January 2016 to December 2018. We tested 07 different ML models, namely, Classification and Regression Tree (CART), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Adaptive Boosting (AB), Random Forests (RF) and, Gradient Boosting (GB). We used a cross-validation strategy. Our work demonstrated that ML algorithms could perform well in assessing the sex of patients undergoing myocardial scintigraphy exams. All the models had accuracy greater than 82%. However, only SVM achieved 90%. KNN, RF, AB, GB had, respectively, 88, 86, 85, 83%. Accuracy standard deviation was lower in KNN, AB, and RF (0.06). SVM and RF had had the best area under the receiver operating characteristic curve (0.93), followed by GB (0.92), KNN (0.91), AB, and NB (0.9). SVM and AB achieved the best precision. Our results bring some challenges regarding the autonomy of patients who wish to keep sex information confidential and certainly add greater complexity to the debate about what data should be considered sensitive to the light of the GDPR.

This article compares six machine learning (ML) algorithms: Classification and Regression Tree (CART), Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN), Linear Regression (LR) and Multilayer Perceptron (MLP) on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by estimating their classification test accuracy, standardized data accuracy and runtime analysis. The main objective of this study is to improve the accuracy of prediction using a new statistical method of feature selection. The data set has 32 features, which are reduced using statistical techniques (mode), and the same measurements as above are applied for comparative studies. In the reduced attribute data subset (12 features), we applied 6 integrated models AdaBoost (AB), Gradient Boosting Classifier (GBC), Random Forest (RF), Extra Tree (ET) Bagging and Extra Gradient Boost (XGB), to minimize the probability of misclassification based on any single induced model. We also apply the stacking classifier (Voting Classifier) ​​to basic learners: Logistic Regression (LR), Decision Tree (DT), Support-vector clustering (SVC), K-Nearest Neighbors (KNN), Random Forest (RF) and Naive Bays (NB) to find out the accuracy obtained by voting classifier (Meta level). To implement the ML algorithm, the data set is divided in the following manner: 80% is used in the training phase and 20% is used in the test phase. To adjust the classifier, manually assigned hyper-parameters are used. At different stages of classification, all ML algorithms perform best, with test accuracy exceeding 90% especially when it is applied to a data subset.

ObjectiveTo investigate the determinants affecting live birth outcomes in fresh embryo transfer among polycystic ovary syndrome (PCOS) patients using various machine learning (ML) algorithms and to construct predictive models, offering novel insights for enhancing live birth rates in this specific group.MethodsA sum of 1,062 fresh embryo transfer cycles involving PCOS patients were analyzed, with 466 resulting in live births. The dataset was split randomly into training and testing subsets at a 7:3 ratio. Least absolute shrinkage and selection operator and recursive feature elimination methods were utilized for feature selection within the training data. A grid search strategy identified the optimal parameters for seven ML models: decision tree (DT), K-nearest neighbors (KNN), light gradient boosting machine (LightGBM), naive Bayes model(NBM), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost). The evaluation of model effectiveness incorporated diverse metrics, encompassing area under the curve (AUC), accuracy, positive predictive value, negative predictive value, F1 score, and Brier score. Calibration curves and decision curve analysis were employed to ascertain the optimal model. Furthermore, Shapley additive explanations were applied to elucidate the importance of predictor variables in the top-performing model.ResultsThe AUC values of DT, KNN, LightGBM, NBM, RF, SVM and XGBoost models in the training set were 0.813, 1.000, 0.724, 0.791, 1.000, 0.819 and 0.853, respectively. Corresponding values in the testing set were 0.773, 0.719, 0.705, 0.764, 0.794, 0.806 and 0.822. XGBoost emerged as the most effective ML model. SHAP analysis revealed that variables encompassing embryo transfer count, embryo type, maternal age, infertility duration, body mass index, serum testosterone (T) levels, and progesterone (P) levels on the day of human chorionic gonadotropin administration were pivotal predictors of live birth outcomes in individuals with PCOS receiving fresh embryo transfer.ConclusionThis study developed a live birth prediction model tailored for PCOS fresh embryo transfer cycles, leveraging ML algorithms to compare the efficacy of multiple models. The XGBoost model demonstrated superior predictive capacity, enabling prompt and precise identification of critical risk factors influencing live birth outcomes in PCOS patients. These findings offer actionable insights for clinical intervention, guiding strategies to improve pregnancy outcomes in this population.Clinical trial numberNot applicable.

As the internet continues to play an integral role in our daily lives, the proliferation of phishing websites poses a significant threat to online security which needs to be addressed. This paper predicts phishing websites using Machine Learning (ML), and ensemble ML algorithms by utilizing an open dataset from Kaggle Competition. The study utilizes a varied array of ML algorithms, encompassing Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machine (GBM) to classify features indicative of phishing activities. Additionally, we propose two Stacking ensemble learning by combining ML models. The first one namely ESM1 which combines DT, KNN, SVM as base learners and LR as meta learner and second one namely ESM2 uses GBM, KNN, SVM as base learners and LR as meta learner. The performance of the used models are compared based on several matrices including accuracy, precision, recall, F1 score and AUC values. Among the trained models the ESM2 model outperformed others with a value of 0.98 for all performance measures and closely followed by individual RF and GBM with a value of 0.98 for all metrices. This research contributes to the ongoing efforts to enhance cybersecurity measures by leveraging the power of machine learning and stacking ensemble learning for the proactive identification and mitigation of online phishing threats.

Background and Objectives: Chronic lower back pain (LBP) is a common clinical disorder. The early identification of patients who will develop chronic LBP would help develop preventive measures and treatment. We aimed to develop machine learning models that can accurately predict the risk of chronic LBP. Materials and Methods: Data from the Sixth Korea National Health and Nutrition Examination Survey conducted in 2014 and 2015 (KNHANES VI-2, 3) were screened for selecting patients with chronic LBP. LBP lasting >30 days in the past 3 months was defined as chronic LBP in the survey. The following classification models with machine learning algorithms were developed and validated to predict chronic LBP: logistic regression (LR), k-nearest neighbors (KNN), naïve Bayes (NB), decision tree (DT), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), and artificial neural network (ANN). The performance of these models was compared with respect to the area under the receiver operating characteristic curve (AUROC). Results: A total of 6119 patients were analyzed in this study, of which 1394 had LBP. The feature selected data consisted of 13 variables. The LR, KNN, NB, DT, RF, GBM, SVM, and ANN models showed performances (in terms of AUROCs) of 0.656, 0.656, 0.712, 0.671, 0.699, 0.660, 0.707, and 0.716, respectively, with ten-fold cross-validation. Conclusions: In this study, the ANN model was identified as the best machine learning classification model for predicting the occurrence of chronic LBP. Therefore, machine learning could be effectively applied in the identification of populations at high risk of chronic LBP.

Purpose: Weather information plays a crucial role in the human society. It helps to lower the weather related losses and enhance the societal benefits such as the protection of life, health, property, etc., It is very much essential for the proper classification of weather images (WIs) into several categories such as dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, snow, etc. so that appropriate information can be provided to the people as well as organizations for further analysis. Approach: In this work, a machine intelligent (MI) based approach is proposed for the classification of WIs into the dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, and snow types. The proposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is compared with other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Result: The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 1604 WIs having 149, 141, 146, 150, 144, 146, 142, 147, 149, 147, 143 numbers of dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, and snow types respectively are taken from the Kaggle source. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA, F1, PR and RC as compared to other ML based methods such as LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGD. Originality: In this work, a MI based approach is proposed by focusing on the stacking of LRG, SVMN, RFS and NNT methods to carry out the classification of WIs into several types such as dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, and snow type. The proposed approach performs better in terms of CA, F1, PR and RC as compared to LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGDC methods. Paper Type: Conceptual Research.

Purpose: Breast Cancer (BC) is considered as one of the most dangerous diseases, especially in women. The survivability of the patient is a challenging task if the breast cancer is in severe stage. It is very much essential for the early classification of breast ultrasound images (BUIs) into several categories such as benign (BN), malignant (MG) and normal (NL), etc. so that preventive measures can be taken accordingly at the earliest. Approach: In this work, a machine intelligent (MI) based approach is proposed for the classification of BUIs into the BN, MG and NL types. The proposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is compared with other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Result: The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 750 TLDIs having 250 numbers of each type such as BN, MG and NL are taken from the Kaggle source. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA, F1, PR and RC as compared to other ML based methods such as LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGD. Originality: In this work, a MI based approach is proposed by focusing on the stacking of LRG, SVMN, RFS and NNT methods to carry out the classification of BUIs into several types such as BN, MG and NL. The proposed approach performs better in terms of CA, F1, PR and RC as compared to LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGDC methods. Paper Type: Conceptual Research.

Purpose: Vegetable production plays a vital role for the existence of human society. It is very much essential for the proper care of vegetable plants for better production of vegetables. However, vegetable plant leaf disease is a major concern in the current scenario. Tomato leaf disease is one of them. So, preventive measures should be taken to avoid the rise of tomatoes and other leaf diseases at the earliest for better production of vegetables. Approach: In this work, a machine intelligent (MI) based approach is proposed for the classification of tomato leaf disease images (TLDIs) into the bacterial spot (BS), early blight (EB), late blight (LB), leaf mold (LM), septoria leaf spot (SLS), tomato mosaic virus (TMV), tomato yellow leaf curl virus (TYLCV) and healthy (HL) types. The proposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is compared with other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Result: The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 1600 TLDIs having 200 numbers of each type such as BS, EB, LB, LM, SLS, TMV, TYLCV and HL are taken from the Kaggle source. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA, F1, PR and RC as compared to other ML based methods such as LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGD. Originality: In this work, a MI based approach is proposed by focusing on the stacking of LRG, SVMN, RFS and NNT methods to carry out the classification of TLDIs into several types such as BS, EB, LB, LM, SLS, TMV, TYLCV and HL. The proposed approach performs better in terms of CA, F1, PR and RC as compared to LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGDC methods. Paper Type: Conceptual Research.

To identify which Machine Learning (ML) algorithms are the most successful in predicting and diagnosing breast cancer according to accuracy rates. The "College of Wisconsin Breast Cancer Dataset", which consists of 569 data and 30 features, was classified using Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Multilayer Perceptron (MLP), Linear Discriminant Analysis (LDA), XgBoost (XGB), Ada-Boost (ABC) and Gradient Boosting (GBC) ML algorithms. Before the classification process, the dataset was preprocessed. Sensitivity, accuracy, and definiteness metrics were used to measure the success of the methods. Compared to other ML algorithms used in the study, the GBC ML algorithm was found to be the most successful method in the classification of tumors with an accuracy of 99.12%. The XGB ML algorithm was found to be the lowest method with an accuracy rate of 88.10%. In addition, it was determined that the general accuracy rates of the 11 ML algorithms used in the study varied between 88-95%. When the results obtained from the ML classifiers used in the study are evaluated, the efficiency of the GBC algorithm in the classification of tumors is obvious. It can be said that the success rates obtained from 11 different ML algorithms used in the study are valuable in terms of being used to predict different cancer types.

BackgroundAcute upper gastrointestinal bleeding (AUGIB) is one of the most common critical diseases encountered in the intensive care unit (ICU), with a mortality rate ranging from 15 to 20%. Accurate stratification of acute gastrointestinal bleeding into acute variceal gastrointestinal bleeding (AVGIB) and acute non-variceal gastrointestinal bleeding (ANGIB) subtypes is clinically essential as distinct entities require markedly different therapeutic approaches and even divergent prognostic implications. AUGIB characterized by hemorrhagic shock, hypotension, multiple organ dysfunction (MODS), and even circulatory failure is life-threatening. Machine learning (ML) prediction model can be an effective tool for mortality prediction, enabling the timely identification of high-risk patients and improving outcomes.MethodsA total of 3,050 acute upper gastrointestinal bleeding (AUGIB) patients were included in our research from the MIMIC-IV database, among which 625 patients were classified as AVGIB and 2,425 patients were categorized as ANGIB. Patients’ clinical features, intervention methods, vital signs, scores, and important laboratory results were collected. The Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors (SMOTE-ENN) and Adaptive Synthetic Sampling (ADASYN) were adopted to address the imbalance of the dataset. As many as 12 machine learning (ML) algorithms, namely, logistic regression (LR), decision tree (DT), random forest (RF), gradient boosting (GB), AdaBoost, XGBoost, Naive Bayes (NB), support vector machine (SVM), light gradient-boosting machine (LightGBM), K-nearest neighbors (KNN), extremely randomized trees (ET), and voting classifier (VC), were performed. The model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Shapley Additive exPlanations (SHAP) analysis was conducted to identify the most influential features contributing to mortality prediction.ResultsIn terms of AVGIB patients, extremely randomized trees model demonstrated excellent predictive value among other ML models, with the AUC of 0.996 ± 0.007, accuracy of 0.996 ± 0.009, precision of 0.957 ± 0.024, recall of 0.988 ± 0.012, and F1 score of 0.972 ± 0.007. The top 10 primary feature variables of ET model were whether combined with acute kidney failure, transfusion of albumin, vasoactive drugs, transfusion of plasma, transfusion of platelet, the max of international normalized ratio (INR), the max of prothrombin time (PT), and the max of activated partial thromboplastin time (APTT). In case of ANGIB patients, gradient boosting model proven to be the optimal machine learning models, with the AUC of 0.985 ± 0.002, accuracy of 0.948 ± 0.009, precision of 0.949 ± 0.009, recall of 0.968 ± 0.009, and F1 score of 0.959 ± 0.007. Similarly, the top 10 feature variables of GB model were Glasgow Coma Scale (GCS) score, vasoactive drugs, acute kidney failure, AIMS65 score, APACHE-II score, mechanical ventilation, the minimum of lactate, chronic liver disease, and the minimum and maximum of APTT. The SHAP visualization shows the weights of two ML models feature variables and the average sharp values of variables. Meanwhile, SHAP waterfall outputs the model prediction process with true positive and negative patients. Most importantly, two website prognostic prediction platforms were developed to enhance clinical accessibility: the ET model for AVGIB patients available at https://10zr656do5281.vicp.fun while the GB model for ANGIB patients accessible at http://10zr656do5281.vicp.fun.ConclusionThe ET model provides a reliable prognostic tool for AVGIB patients, while the GB model serves as a robust tool for ANGIB patients in predicting in-hospital mortality. By systematically integrating clinical features, risk stratification scores, vital signs, and invention measures, the ML models may deliver comprehensive predictions that benefit for clinical decision-making and potentially enhance clinical outcomes in the near future.

To explore the value of machine learning (ML) models based on contrast-enhanced cone-beam breast computed tomography (CE-CBBCT) radiomics features for the preoperative prediction of human epidermal growth factor receptor 2 (HER2)-low expression breast cancer (BC). Fifty-six patients with HER2-negative invasive BC who underwent preoperative CE-CBBCT were prospectively analyzed. Patients were randomly divided into training and validation cohorts at approximately 7:3. A total of 1046 quantitative radiomic features were extracted from CE-CBBCT images and normalized using z-scores. The Pearson correlation coefficient and recursive feature elimination were used to identify the optimal features. Six ML models were constructed based on the selected features: linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), logistic regression (LR), AdaBoost (AB), and decision tree (DT). To evaluate the performance of these models, receiver operating characteristic curves and area under the curve (AUC) were used. Seven features were selected as the optimal features for constructing the ML models. In the training cohort, the AUC values for SVM, LDA, RF, LR, AB, and DT were 0.984, 0.981, 1.000, 0.970, 1.000, and 1.000, respectively. In the validation cohort, the AUC values for the SVM, LDA, RF, LR, AB, and DT were 0.859, 0.880, 0.781, 0.880, 0.750, and 0.713, respectively. Among all ML models, the LDA and LR models demonstrated the best performance. The DeLong test showed that there were no significant differences among the receiver operating characteristic curves in all ML models in the training cohort (P > .05); however, in the validation cohort, the DeLong test showed that the differences between the AUCs of LDA and RF, AB, and DT were statistically significant (P = .037, .003, .046). The AUCs of LR and RF, AB, and DT were statistically significant (P = .023, .005, .030). Nevertheless, no statistically significant differences were observed when compared to the other ML models. ML models based on CE-CBBCT radiomics features achieved excellent performance in the preoperative prediction of HER2-low BC and could potentially serve as an effective tool to assist in precise and personalized targeted therapy.

The precise diagnosis of heart disease represents a significant obstacle within the medical field, demanding the implementation of advanced diagnostic instruments and methodologies. This article conducts an extensive examination of the efficacy of different machine learning (ML) and deep learning (DL) models in forecasting heart disease using tabular dataset, with a particular focus on a binary classification task. An extensive array of preprocessing techniques is thoroughly examined in order to optimize the predictive models’ quality and performance. Our study employs a wide range of ML algorithms, such as Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neibors (KNN), AdaBoost (AB), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), CatBoost (CB), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN) to assess the predictive performance of these algorithms in the context of heart disease detection. By subjecting the ML models to exhaustive experimentation, this study evaluates the effects of different feature scaling, namely standardization, minmax scaling, and normalization technique on their performance. The assessment takes into account various parameters including accuracy (Acc), precision (Pre), recall (Rec), F1 score (F1), Area Under Curve (AUC), Cohen’s Kappa (CK)and Logloss. The results of this research not only illuminate the optimal scaling methods and ML models for forecasting heart disease, but also offer valuable perspectives on the pragmatic ramifications of implementing these models within a healthcare environment. The research endeavors to make a scholarly contribution to the field of cardiology by utilizing predictive analytics to pave the way for improved early detection and diagnosis of heart disease. This is critical information for coordinating treatment and ensuring opportune intervention.

Predicting mortality is vital for tailoring treatments, improving care and reducing costs. Machine learning (ML) has shown strong potential, often outperforming traditional severity-of-illness scoring systems in intensive care units (ICUs). However, the black-box nature of ML limits adoption. This study evaluates the accuracy of several ML algorithms on the Medical Information Mart for Intensive Care III (MIMIC-III) dataset and applies explainable artificial intelligence to identify variables influencing ICU mortality. A retrospective cohort of 600 MIMIC-III patient records was analysed. ML algorithms tested included support vector machine (SVM), K-nearest neighbour (KNN), decision trees (DT), gradient boosting (GB), random forests (RF), Naive Bayes (NB), logistic regression (LR) and extra trees (ET). Models were assessed with threefold cross-validation using F1 Score, sensitivity, specificity, confusion matrix and accuracy. SHapley Additive exPlanations (SHAP) was applied to explain key factors influencing predictions. Among the eight algorithms evaluated, ET and GB achieved the highest accuracy (98.33% and 98.23%, respectively) with F1-scores above 96%. SVM also performed strongly (97.50% accuracy, F1=94.34%). RF and DT yielded accuracies of 95.00% and 94.17%, respectively. NB reached 96.67% accuracy with 100% recall, while KNN and LR showed lower discriminative performance (76.67% and 75.83% accuracy, respectively).SHAP, LIME and Shapley values consistently identified hypertension, tumours and endocrine and digestive disease as the leading predictors of mortality. Findings highlight ML's potential to optimise ICU decision-making and support clinicians. However, reliance on retrospective data remains a limitation, and clinical validation is required. ML algorithms are highly effective for mortality prediction, and explainability is key for trust and adoption. When combined, accuracy and interpretability enable ML to safely support informed ICU decisions and improve patient outcomes.