ILABCARE: A Comprehensive ML/DL –Based Prediction System for Healthcare

Abstract

Effective healthcare decision-making [10] can be challenging due to the vast complexity and volume of health data. Traditional methods of disease diagnosis and risk assessment often rely on manual analysis and expert interpretation, which can be time-consuming and prone to human error. This is especially critical when managing chronic conditions such as heart [1] disease, diabetes [3], Parkinson’s [2], and cardiovascular disease [4], where early detection can significantly improve outcomes. This research presents a multi-disease prediction tool that utilizes advanced machine learning [8] techniques, including Logistic Regression [6] and Support Vector Machine (SVM [7]), to provide accurate predictions of disease risk based on user-provided data. The tool offers real-time insights by analyzing demographic, medical, and lifestyle factors, facilitating proactive health management. Data preprocessing strategies, such as handling data imbalances and feature scaling [16], were employed to improve model accuracy. By providing reliable predictions, this tool aids healthcare providers and individuals in making informed decisions for early intervention. The project demonstrates the potential of machine learning [8] in transforming healthcare practices by enabling accessible, data-driven disease risk assessments.