Heart Stroke Cardiovascular Predictive System Using Multi-Model Supervised Techniques

Abstract- Heart stroke and cardiovascular diseases rank among the primary factors contributing to mortality of death worldwide. Factors such as changing lifestyles, environmental pollution, increased stress levels, and inadequate access to healthcare in certain regions can contribute to the growing incidence of heart diseases. For prevention and prompt intervention, predictive systems that can precisely identify people who are at a high risk of contracting these diseases are crucial. The traditional approach to diagnosing heart disease may not always provide accurate results and the process would be very time-consuming and costly and is not even feasible in multiple regions. The proposed model will not only assist but also ensure accurate prediction about the heart condition of the patients. Then we used the algorithm that provided the most accuracy. The model undergoes training so that the diagnosis is possibly made using the information provided by the patients. We assess our method using an extensive data collection of patients with and without heart stroke and cardiovascular diseases and show that our method outperforms existing predictive systems [1].