6. HEART DISEASE DETECTION USING AI AND ML

Heart disease is one of the leading causes of mortality worldwide. Early The chance of fatal consequences might be considerably decreased with detection and precise diagnosis. In order to categorise patients ac cording to their risk of developing heart disease, this study proposes a predictive model based on AI and ML that examines medical data. The model makes use of machine learning techniques, including Decision Trees, Random Forest, Support Vector Machine (S VM), and Deep Learning models, to provide an efficient and accurate diagnosis. The dataset used in this study includes patient health parameters such as age, blood pressure, cholesterol levels, heart rate, and other vital indicators. Feature engineering te chniques have been applied to refine the dataset, and hyperparameter tuning has been used to optimize the models. Experimental results indicate that the AI-driven model achieves an accuracy of over 90%, making it a reliable tool for heart disease predictio n. This research demonstrates the potential of machine learning in healthcare, aiding medical professionals in early diagnosis and treatment planning.