Heart disease has been a major life threatening problem in a human being. Due to several reasons such as eating habits, lack of exercise, becoming overweight, and smoking and unhealthy lifestyle habits cause's heart diseases. This motivates us to get a clear idea of the risk factors in our life regarding the cause of heart disease. The proposed method suggests the levels of risk factors according the patients data, so that one can take care of their health properly in order to prevent the heart disease. In this research work, proposed methodology is to segregate heart disease patients based on their risk factors. Firstly applying the feature selection, this is to reduce the records of data and to train the model faster. The five classifiers are support vector machine, K-nearest neighbor, decision tree and random forest are applied. After applying the classifiers a proposed model will segregate the heart disease patients based on their risk factors according to age of the patients. This will be helping for the doctors to analyze the risk factors of the patients.

Earlier Prediction on the heart disease based on supervised machine learning techniques

Heart disease is one of the major causes of death throughout the world. It cannot be easily predicted by the medical practitioners as it is a difficult task which demands expertise and higher knowledge for prediction. An automated system in medical diagnosis would enhance medical efficiency and also reduce costs. We will design a system that can efficiently discover the rules to predict the risk level of patients based on the given parameters about their health. The goal is to extract hidden patterns by applying data mining techniques, which are noteworthy to heart diseases and to predict the presence of heart disease in patients where the presence is valued on a scale. The prediction of heart disease requires a huge size of data which is too complex and massive to process and analyze by conventional techniques. Our objective is to find out the suitable machine learning technique that is computationally efficient as well as accurate for the prediction of heart disease. Data mining combines Statistical analysis machine learning and database technology to extract hidden patterns and relationships from large databases. The implementation of work is done on Cleveland heart diseases data set from the University of California Irvine (UCI) machine learning repository to test on different data mining techniques.

https://www.jetir.org/papers/JETIR2006301.pdf

Heart disease prediction using machine learning

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Heart disease prediction using machine learning