Abstract

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of deaths in the world over the last many decades and has emerged as the most life- threatening disease, not only in India but also in the whole world. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. So, there's a need of dependable, accurate and possible system to diagnose similar diseases in time for proper treatment. Machine Learning algorithms and approaches have been applied to various medical datasets to automate the analysis of large and complex data. multiple experimenters, in recent times, have been using several machine learning approaches to help the health care industry and the professionals in the diagnosis of heart related diseases. This project presents a review of various models based on like algorithms and approaches and analyses their performance. The main aim of this design is to give an effective algorithm to predict heart disease. So, at the end we compare our algorithm (Genetic algorithm) with BAT and BEE algorithms and we prove that the produced algorithm is effective one among all. Also, we forecast the output by taking some random data. Keywords: Cardiovascular Diseases, Machine Learning Algorithms, Genetic Algorithm,

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