International Research Journal of Education and Technology

Heart disease has climbed its way to the top of the list of the primary causes of death all over the world. In the past, individuals also referred to heart disease as cardiovascular disease when talking about it. According to the WHO, heart disease is to blame for 31% of all deaths worldwide. Heart disease and stroke are the primary causes of death in India, together accounting for one death in every four that occur there .Heart disease is a serious condition that often goes undetected through physical observation alone, as it typically manifests without clear symptoms until it reaches critical stages. DM using To prevent sudden cardiac events, early and accurate diagnosis is essential. This diagnosis requires the careful analysis of a variety of clinical and pathological data, such as electrocardiograms (ECG), blood pressure, cholesterol levels, blood sugar levels, and imaging tests like echocardiograms or angiograms. These complex datasets must be interpreted by medical experts, making the diagnostic process both time-consuming and challenging.The proposed system uses machine learning algorithms & AI to predict cardiovascular disease (CVD) by analyzing patient data such as medical history, lifestyle, and clinical test results. It leverages the Cleveland heart disease dataset to identify patterns through feature integration, including factors like cholesterol levels and ECG readings. Advanced classification techniques like decision trees and neural networks enhance prediction accuracy. The system allows for real-time data input from wearable devices, supporting early diagnosis and personalized treatment, ultimately helping reduce CVD-related mortality. the proposed approach showed to be accurate in predicting heart disease. In all cases, Python was used.