HEALTH DATA INFORMATION & MANAGEMENT SYSTEM

Abstract: A Health Data Information and Management System (HDIMS) can centralize these records and improve their organization, retrieval and controlled sharing. However, conventional information-management systems generally focus on storing and retrieving records and do not fully exploit the analytical value of structured healthcare data. This research proposes an enhanced HDIMS that integrates a machine-learning module for heart disease risk prediction. The proposed workflow includes data collection, cleaning, exploratory data analysis, feature preparation, model training, comparative evaluation and integration of the selected model into the HDIMS interface. Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine and XGBoost are proposed for comparison. Evaluation will use accuracy, precision, recall, F1-score, confusion matrix and ROC-AUC, with particular attention to recall because false- negative risk can be important in screening-oriented applications. Explainable AI, such as SHAP, is proposed to help communicate the factors contributing to a prediction. The literature review indicates that machine learning has substantial research support for cardiovascular risk prediction, while external validation, data quality, interpretability, privacy and clinical translation remain important challenges. The proposed contribution is therefore not the invention of heartdisease prediction itself, but the practical integration of an interpretable, reproducible predictive workflow into a health- information management platform. The output is intended as an academic decision-support and risk-estimation feature and not as a medical diagnosis.

Keywords: Health Data Information Management, HDIMS, Heart Disease Prediction, Machine Learning, Healthcare Analytics, Electronic Health Records, Explainable AI, Risk Prediction.