SLIIT International Conference On Engineering and Technology Vol. 04 [SICET] 2025

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/5064

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Subject: search.filters.subject.Machine Learning

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    PublicationOpen Access
    A Data-Driven Approach to Predicting Ischemic Heart Disease Risk in Monaragala: Integrating Lifestyle and Symptom Factors with Machine Learning
    (Faculty of Engineering, 2025-09-09) Meddepola, M.A.R.L.; Wickramasinghe, B.M.G.S.T.S.K.
    Ischemic Heart Disease (IHD) remains a leading cause of mortality worldwide and presents a critical challenge in underserved rural areas such as Monaragala, Sri Lanka. Traditional IHD prediction methods predominantly depend on clinical diagnostics like ECGs and blood tests, which are often unavailable or inaccessible in such regions. This study aims to bridge this gap by developing a machine learning-based prediction model that utilizes only lifestyle and symptom-related data, eliminating the need for invasive clinical procedures. A dataset comprising lifestyle habits (e.g., diet, smoking, alcohol use, exercise) and symptom indicators (e.g., chest pain, fatigue, dizziness) was collected via surveys. Feature selection using Logistic Regression identified the top eight most relevant predictors. Five machine learning algorithms, Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest, were trained and evaluated. Among them, the Random Forest model achieved the highest performance with an accuracy of 83.5%, precision of 0.86, recall of 0.78, and F1- score of 0.81, demonstrating strong predictive capability based solely on non-clinical features. In addition, a web-based self-assessment tool was developed to make the model accessible to the public, particularly targeting individuals in rural areas with limited healthcare access. The tool enables users to input basic lifestyle and symptom information and receive a real-time risk assessment. The findings confirm that the model leveraging lifestyle and symptom data can effectively identify individuals at risk of IHD. This approach supports the development of scalable, low-cost, and user-friendly screening tools that can enhance early detection and preventive care, especially in rural and resource-constrained settings.
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    PublicationOpen Access
    Predictive Modeling for Personalized Cancer Therapy Using Reinforcement Learning
    (Faculty of Engineering, 2025-09-09) Edirisinghe M.M; Gunarathne, J H M S M
    Adaptive therapy is transforming cancer treatment by enabling dynamic, patient-specific interventions that adapt to tumor progression and individual variability. Unlike traditional fixed-dose regimens, adaptive therapy leverages the evolutionary dynamics of tumors to extend treatment effectiveness and delay resistance. Reinforcement Learning (RL), an area of artificial intelligence focused on sequential decision-making, offers a robust framework for optimizing these adaptive strategies. RL can learn optimal treatment policies by interacting with computational models of tumor growth and drug response, continuously adjusting regimens based on observed tumor states, resistant cell populations, and biomarkers. This approach allows for the creation of personalized therapies that maintain long-term tumor control while minimizing toxicity and the emergence of resistance. The integration of RL into predictive modeling for cancer therapy represents a paradigm shift, enabling smarter, safer, and more effective treatments that are dynamically tailored to each patient’s evolving disease. This paper reviews the foundational concepts of adaptive therapy and RL discusses tumor modeling approaches, examines RL algorithms, and addresses current challenges and future directions in the field.
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    PublicationOpen Access
    Enhancing Healthcare Predictive Models Through Privacy- Preserving Synthetic Data Generation
    (Faculty of Engineering, 2025-09-09) Edirisinghe M.M; Gunarathne J H M S M; Wanniarachchi W A A M
    The advancement of healthcare predictive modeling is closely tied to the availability and quality of patient data. However, privacy regulations and ethical concerns often hinder data sharing, making it a persistent challenge. As a solution, privacy-preserving synthetic data generation has emerged, enabling the creation of artificial datasets that retain the statistical properties of real data while protecting individual privacy. This paper explores the use of such synthetic data throughout the clinical risk prediction pipeline by leveraging state-of-the-art generative models. We evaluate their utility in exploration data analysis, feature selection, model training, and deployment. Our study focuses on synthetic data generated using advanced models such as Differentially Private GANs (DPGAN), Private Aggregation of Teacher Ensembles GANs (PATEGAN), and Anonymization through Data Synthesis GANs (ADSGAN). Using these techniques, we created synthetic versions of the UK Biobank ever- smoker cohort. These synthetic datasets were shown to reproduce key statistical patterns, support effective feature selection, and enable accurate lung cancer risk prediction modeling all without using real patient data. We compare synthetic data with other privacy-enhancing technologies like federated learning and highlight a key advantage: synthetic data allows the direct use of existing analytical and machine learning tools without modification. Additionally, we examine deployment models such as "no- release" and "delayed-release," emphasizing how synthetic data can speed up research and enable broader data sharing while maintaining GDPR compliance. Overall, this study demonstrates the potential of synthetic data to transform healthcare research, software testing, education, and collaboration while carefully navigating the trade-off between privacy and utility.