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Heart Disease Detection Using Machine Learning & Data Mining Techniques

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Heart disease diagnosis is regarded as an important yet complicated job that needs to be done accurately and in faster way. The advancement of that kind of system will be very advantageous and helpful. Medical experts and professionals don’t have expertise in all sub specialties and moreover there is a shortage of resource persons at certain places. So we have produce some kind of system that can diagnose heart disease in earlier stages and doctors can be assisted by that kind of system. So this kind of intelligent disease detection system could be very beneficial in medical treatment.

Nowadays people looking after money, in this situation they actually ignore their health care. That’s why people are suffering with lot of diseases. Because of unhealthy lifestyle and unhealthy food consumption they are suffering various heart diseases like other diseases. Heart disease has some types and condition. Heart disease was the major cause of casualties in the different countries including India. Heart disease kills person every 34 seconds in the United States.

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One of the commonly occurred heart diseases is Cardio vascular disease. Thus it is highly essential to predict such diseases through suitable symptoms. There are various types of algorithms which are present for the prediction of heart diseases which are Decision Trees, Naïve Bayes etc. Coronary heart disease, Cardiomyopathy and Cardiovascular disease are some categories of heart diseases. Coronary heart disease is responsible for one of every five deaths in the United States.

The availability of huge amounts of medical data leads to the need for powerful data analysis tools to extract useful knowledge. Researchers have long been concerned with applying statistical and data mining tools to improve data analysis on large data sets. Disease diagnosis is one of the applications where data mining tools are proving successful results. Heart disease is the leading cause of death all over the world in the past ten years. Several researchers are using statistical and data mining tools to help health care professionals in the diagnosis of heart disease. Using single data mining technique in the diagnosis of heart disease has been comprehensively investigated showing acceptable levels of accuracy. Recently, researchers have been investigating the effect of hybridizing more than one technique showing enhanced results in the diagnosis of heart disease. However, using data mining techniques to identify a suitable treatment for heart disease patients has received less attention.

In healthcare institutes health care practitioners detect or identify heart diseases by analyzing heart test results. In this type of prediction and decision making machine learning and data mining techniques can increase the disease detection accuracy rate. A Naive Bayes classifier predicts that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature. This will help to predict most accurate disease detection. There we also can use decision tree technics to help doctor to make best decision which will be helpful to detect diseases in early stage and also save life and money. Weighted fuzzy rules can be used to make decision based on previous medical data. The proposed clinical decision support system for the risk prediction of heart patients consists of two phases: automated approach for the generation of weighted fuzzy rules and developing a fuzzy rule-based decision support system. A simple coronary disease prediction algorithm was developed using categorical variables, which allows physicians to predict multivariate CHD risk in patients without overt CHD.

In hospitals data are store in digital format but it doesn’t used in proper way. By developing this system using those previous data record we can early detect those heart diseases. We present a Two-Stage Machine Learning (ML) model as a data mining method to develop practice guidelines and apply it to the problem of dementia staging. Dementia staging in clinical settings is at present complex and highly subjective because of the ambiguities and the complicated nature of existing guidelines. Our model abstracts the two-stage process used by physicians to arrive at the global Clinical Dementia Rating Scale (CDRS) score. The World Health Organization has estimated that 12 million deaths occur worldwide, every year due to the Heart diseases. Half the deaths in the United States and other developed countries occur due to cardio vascular diseases. It is also the chief reason of deaths in numerous developing countries. It is possible to predict the efficiency of medical treatments by building the data mining applications. Data mining can deliver an assessment of which courses of action prove effective by comparing and evaluating causes, symptoms, and courses of treatments. The real life data mining applications are attractive since they provide data miners with varied set of problems, time and again. Working on heart disease patients databases is one kind of a real-life application. The detection of a disease from several factors or symptoms is a multi-layered problem and might lead to false assumptions frequently associated with erratic effects. Therefore it appears reasonable to try utilizing the knowledge and experience of several specialists collected in databases towards assisting the diagnosis process. So that’s how by this system we can improve heart disease detection and make decision making easier and accurate for doctor.


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