Article
HEALTH RISK PREDICTION SYSTEM USING MACHINE LEARNING
The rapid advancement of healthcare technologies and the increasing availability of patient data have created new opportunities for predictive analytics in medical diagnosis. Health risk prediction using Machine Learning (ML) has emerged as a powerful approach to identify potential diseases at an early stage, thereby improving patient outcomes and reducing healthcare costs. This paper presents a Health Risk Prediction System that utilizes machine learning algorithms to analyze patient health parameters and predict the likelihood of diseases such as heart disease, diabetes, and hypertension. Traditional healthcare systems rely heavily on manual diagnosis, which can be time-consuming, error-prone, and often delayed. In contrast, the proposed system automates the prediction process by leveraging supervised learning techniques and data-driven decisionmaking. The system collects patient data including age, blood pressure, blood sugar levels, cholesterol, heart rate, and Body Mass Index (BMI). This data is preprocessed and transformed into a structured format suitable for machine learning models. Various algorithms such as Logistic Regression, Decision Trees, and Support Vector Machines are applied to classify patients into risk categories such as low risk and high risk. The system is implemented using Python programming language with a Flask-based web interface and SQLite database for efficient data storage and retrieval. Experimental results demonstrate that the proposed system achieves high accuracy and reliability in predicting health risks. The system not only reduces the burden on healthcare professionals but also enables early disease detection and preventive care. Furthermore, the integration of machine learning with healthcare systems enhances decision-making capabilities and supports continuous monitoring of patient health. The proposed model is scalable and can be extended to incorporate additional medical parameters and advanced deep learning techniques. Overall, this research contributes to the development of intelligent healthcare systems that improve diagnosis, reduce human error, and promote proactive health management.
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