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Subject: search.filters.subject.Random Forest

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    Enhancing Chronic Kidney Disease Prediction : A Hybrid Approach Combining Logistic Regression and Random Forest Models
    (Institute of Electrical and Electronics Engineers Inc., 2025) Jathunga, T; Abeygunawardena, N
    This study investigates the use of Machine Learning (ML) models for Chronic Kidney Disease (CKD) prediction, comparing Logistic Regression with L1 and L2 regularization, Random Forest , and a Hybrid Voting Classifier. The models were evaluated using performance metrics including accuracy, precision, recall, and F1-score, with the hybrid model demonstrating the highest accuracy of 99 percent, followed by Random Forest at 98 percent. Logistic Regression models achieved accuracies of 97 percent and 98 percent , with slight variations in recall for different classes. Cross-validation and learning curve analyses indicated minimal overfitting in ensemble models. These results emphasize the potential of ML models for accurate CKD prediction, suggesting further research into model optimization and data preprocessing techniques.
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    Developing Predictive Models for Future Stress Likelihood and Recovery Time Using Behavioral and Emotional Data
    (Institute of Electrical and Electronics Engineers Inc., 2025) Weerasinghe W.P.D.J.N; Gunasekera H.D.P.M; Wickramasinghe B.G.W.M.C.R; Jayathunge K.A.D.T.R; Wijesiri, P; Dassanayake, T
    Stress has a serious impact on mental and physical well-being, but treatments as usual are often unavailable and not effective over the long term. The AyurAura application combines imaginative Ayurvedic therapies with modern AI techniques to deliver customized stress reduction by way of Mandala art and music. This research develops two predictive models for the application. In its first model, the stress prediction probability is estimated from users' behavior in a questionnaire and the result can be used to proactively intervene. The second model forecasts time needed for recovery into a stress-free state by using the changes in daily emotional state and participation in app activities. Machine learning algorithms are used to prepare behavioral and emotional data for improved prediction performance. Trained on multi-institution datasets, both models delivered 90-95% accuracy, enabling the user to detect behavior eliciting stress and the degree needed for recovery. These results highlight the possibility of combining conventional therapeutics with contemporary tech for ongoing, affordable stress relief interventions with personalized needs in mind.