Context: Lung cancer is a leading cause of cancer-related mortality world- wide. While machine learning (ML) offers significant potential for improving prediction tasks, comprehensive reviews synthesizing its application across the full spectrum of lung cancer prediction—from diagnosis and risk to sur- vival—using structured data are scarce. Objective: This systematic review aims to comprehensively analyze ML techniques for three key lung cancer prediction tasks—diagnosis, risk assessment, and survival analysis—using structured or tabular data sources. Method: We followed the PRISMA 2020 guidelines, systematically searching five databases (PubMed, Scopus, IEEE Xplore, ACM Digital Library, Science Direct) for studies published by August 2025. From an initial 772 records, 42 studies met our inclusion criteria, which mandated the use of structured data and ML models for pre- diction. Results: Ensemble methods, particularly XGBoost and Random Forest, were the most prevalent and high-performing models across all tasks. However, performance was highly task-dependent. Key predictive features included demographics, clinical parameters, and lifestyle factors. Datasets were also task-specific: SEER and NLST for diagnosis; population registries (e.g., Danish) for risk; and SEER and TCGA for survival. Common val- idation techniques included holdout and cross-validation, with SHAP and LIME emerging as dominant interpretability tools. While many studies re- ported high performance (e.g., accuracies up to 99%), these results must be interpreted with caution due to dataset imbalances and a general lack of external validation. Conclusion: This review provides a structured syn- thesis of ML applications across the lung cancer prediction continuum. It highlights the dominance of ensemble methods and the critical importance of task-specific data and modeling. The findings reveal a pressing need for more rigorous external validation, standardized reporting, and direct com- parison to established clinical models to foster the development of robust, clinically actionable ML tools.

Heart disease is the leading cause of death worldwide, contributing to millions of fatalities each year. Early detection and accurate risk prediction are therefore critical for timely intervention and improved patient outcomes. In this study, seven different Machine Learning (ML) prediction models were tested on the collections of heart disease datasets. The data was divided, with 80% used to train the models and 20% saved to test them later. The settings for the models were carefully adjusted using a method that checks their performance multiple times to find the best ones. The models were then evaluated on the unseen test data. Their performance was measured using several metrics (Accuracy, Recall, Precision, PR-AUC and ROC-AUC), and their results were further examined using a confusion matrix. In addition to traditional evaluation metrics, SHapley Additive exPlanations (SHAP) analysis was employed to interpret the contribution of each feature to the model’s predictions. It was observed that the Multi-Layer Perceptron (MLP) achieved the highest performance on both datasets, demonstrating strong predictive capability while remaining interpretable through the integration of SHAP. This study shows that modern ML models can be very good at predicting heart disease risk, and provide explainable performance. This study provides an effective approach for predicting heart disease risk with an explainable model to help doctors choose the best tool.

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, highlighting the importance of early detection for improving patient survival rates. However, current machine learning approaches for lung cancer prediction often depend on suboptimal model configurations, limited systematic ensemble comparisons, and insufficient interpretability. This study introduces a novel framework, called Lung Explainable Ensemble Optimizer (LungEEO), that integrates three methodological advances: (1) comprehensive hyperparameter optimization across 50 configurations of nine machine learning algorithms for base model selection, (2) a systematic comparison of Hybrid Majority Voting strategies, including unweighted hard voting, weighted hard voting, and soft voting with an ensemble stacking approach, and (3) a dual explainable AI (XAI) layer based on SHAP and LIME to provide parallel global and local explanations. Experiments conducted on two heterogeneous lung cancer datasets indicate that ensemble approaches consistently outperform individual models. Weighted hard voting achieved the best performance on Dataset 1 (Accuracy: 89.04%, F1-Score: 89.04%), whereas ensemble stacking produced superior outcomes on Dataset 2 (Accuracy: 87.95%, F1-Score: 87.95%). Following extensive hyperparameter tuning, Random Forest and Multi-Layer Perceptron performed consistently well as base learners on both datasets. In addition, integrating SHAP with LIME offers additional insights into model behavior, boosting the interpretability of ensemble predictions, and strengthening their potential clinical applicability. To the best of our knowledge, the combined use of these interpretability techniques within an ensemble framework has received limited attention in existing lung cancer prediction studies. Overall, the proposed LungEEO framework offers a promising balance between predictive performance and interpretability, supporting its potential use in clinical decision support.

Obesity is a global health crisis requiring accurate and interpretable prediction models for clinical use. Current ensemble approaches lack systematic comparisons and often use suboptimal configurations. This study introduces OBES-Net, a novel framework that integrates three methodological advances: (1) exhaustive hyperparameter optimization across 50 configurations of nine machine learning algorithms, (2) systematic comparison of Hybrid Majority Voting strategies (Majority Hard vs. Weighted Hard) against Ensemble Stacking, and (3) comprehensive Explainable AI (XAI) validation using SHAP and LIME. Utilizing two diverse datasets, OBES-Net demonstrates that Stacking achieves superior performance on complex data (Accuracy: 0.991 on Dataset-2), while voting strategies remain competitive on simpler tasks. Crucially, the proposed XAI analyses reveal that Stacking’s advantage stems from synthesizing broader clinical risk factors, withWeight and Age emerging as consistent primary drivers. Extensive ablation studies validate the robustness of the proposed design choices. The OBES-Net framework establishes that combining exhaustive optimization with systematic ensemble comparison and XAI delivers both state-of-the-art performance and clinical trustworthiness, providing an advancing methodology for healthcare decision-making.