Predictive Analysis of Cardiovascular Disease

Abstract

Worldwide, heart disease stands as a leading cause of fatalities. The treatment of heart disease necessitates the application of cutting-edge technologies. Within medical centers, a prevailing issue arises where many medical professionals lack the equal knowledge and expertise required for optimal patient care.Consequently, they often make independent decisions, resulting in subpar outcomes and, at times, even fatalities. To address these challenges, the prediction of cardiac illness by applying machine learning methods  and techniques has emerged as a viable solution. These technologies have simplified the process of automatic diagnosis in most of all the hospitals, playing a crucial role in improving patient care. The prognosis of cardiovascular illness relies on the analysis of various health parameters in patients. Numerous algorithms, such as Naïve Bayes, KNN, ANN, are employed for heart disease prediction. Ten metal oxide semiconductor sensors are used in conjunction with a method using artificial neural networks (ANNs) to recognise scent patterns in certain individuals. Our research incorporates diverse parameters, including gender, CP and blood pressure ,age, among others.