SMART HEALTH PREDICTION USING AI

Abstract

Smart health prediction using Artificial Intelligence (AI) has emerged as a transformative approach in modern healthcare systems by enabling early detection, accurate diagnosis, and efficient disease management. With the rapid growth of medical data generated from wearable devices, electronic health records (EHRs), and diagnostic systems, AI techniques such as machine learning and deep learning provide powerful tools to analyze complex datasets and predict potential health risks. This study presents a smart health prediction system that utilizes AI algorithms to monitor patient health conditions and forecast possible diseases at an early stage.The proposed system integrates patient data including physiological parameters, medical history, lifestyle habits, and real-time sensor inputs to generate predictive insights. Machine learning models such as Decision Trees, Support Vector Machines, Random Forests, and Neural Networks are employed to improve prediction accuracy and reliability. The system assists healthcare professionals in making faster and more informed clinical decisions while reducing human error and healthcare costs.Furthermore, the implementation of AIbased smart health prediction enhances personalized treatment planning and supports remote patient monitoring, especially in rural and underserved areas. It also enables proactive healthcare management by identifying risk factors before disease progression. The results demonstrate that AI-driven prediction models significantly improve early diagnosis rates and patient outcomes compared to traditional methods.