Vital Predict-Optimizer: Optimized Health Risk Prediction System

Bonumaddi Kumari 1, Bangari Dinesh Kumar 2

1Assistant Professor, 2 MCA Final Semester, Master of

Computer Applications, Sanketika Vidya Parishad Engineering College, Vishakhapatnam,

Andhra Pradesh, India

bangaridineshkumar@gmail.com

ABSTRACT:

The Vital Predict-Optimizer: Optimized Health Risk Prediction System is a Machine Learning-based healthcare application developed to predict the risk of multiple chronic diseases at an early stage. The system analyzes important health parameters such as age, Body Mass Index (BMI), blood pressure, glucose level, cholesterol level, heart rate, and lifestyle habits including smoking and alcohol consumption. Advanced data preprocessing techniques such as data cleaning, normalization, and feature selection are applied to improve data quality and prediction accuracy. Multiple machine learning algorithms are trained and optimized to enhance the overall performance of the prediction model. The system classifies the health risk into Low, Medium, and High categories based on the user's health profile. Optimization techniques are used to improve model efficiency, reduce prediction errors, and increase reliability. The application provides fast and accurate health risk assessments through a simple and user-friendly interface. It supports early disease detection, enabling users to take preventive healthcare measures before conditions become severe. The system also helps healthcare professionals in making better clinical decisions through intelligent risk analysis. Secure handling of sensitive health information ensures user privacy and data protection. Performance evaluation is carried out using metrics such as Accuracy, Precision, Recall, and F1-Score. The proposed system offers a reliable, scalable, and cost-effective solution for predictive healthcare. Overall, Vital Predict-Optimizer combines Artificial Intelligence, Machine Learning, and optimization techniques to deliver accurate, efficient, and personalized health risk prediction.Top of Form