International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017
p-ISSN: 2395-0072
www.irjet.net
Heart Disease Prediction using Data Mining Sarvesh Chowkekar1, Rohan Ringe2, Viral Gohil3 , Naina Kaushik4 Student VIII SEM, B.E., Computer Engg. , RGIT, Mumbai, India Student VIII SEM, B.E., Computer Engg. , RGIT, Mumbai, India 3 Student VIII SEM, B.E., Computer Engg. , RGIT, Mumbai, India 4 Professor, Department of Computers, RGIT, Mumbai, India 1 2
---------------------------------------------------------------------***--------------------------------------------------------------------2. LITERATURE REVIEW Abstract —Heart disease is a major cause of morbidity and mortality in the modern society. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. All doctors are unfortunately not equally skilled in every sub specialty and they are in many places a scarce resource. Hence this paper presents a technique for prediction of heart disease using major risk factors. This technique involves two most successful data mining tools, neural networks and genetic algorithms. A novel way to enhance the performance of a model that combines genetic algorithms and neuro fuzzy logic for feature selection and classification is proposed. The system implemented uses the global optimization advantage of genetic algorithm for initialization of neural network weights. Keywords - data mining, heart disease, risk factors, prediction, Genetic Algorithms (GA).
1. INTRODUCTION Heart diseases are the number one cause of death globally: more people die annually from Heart diseases than from any other cause. Recent research in the field of medicine has been able to identify risk factors that may contribute toward the development of heart disease but more research is needed to use this knowledge in reducing the occurrence of heart diseases. Data mining is the process of finding previously unknown patterns and trends in databases and using that information to build predictive models. In today's world data mining plays a vital role for prediction of diseases in medical industry. In medical diagnosis, the information provided by the patients may include redundant and interrelated symptoms and signs especially when the patients suffer from more than one type of disease of same category. The physicians may not able to diagnose it correctly. So it is necessary to identify the important diagnostic features of a disease and this may facilitate the physicians to diagnosis the disease early and correctly . Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database. This practice may lead to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patient. Life style risk factors which include eating habits, physical inactivity, smoking, alcohol intake, obesity are major threat in occurrence of heart disease. © 2017, IRJET
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[1] Syed Umar Amin, Kavita Agarwal, Dr. Rizwan Beg This paper presents a technique for prediction of heart disease using major risk factors. This technique involves two most successful data mining tools, neural networks and genetic algorithms. The hybrid system implemented uses the global optimization advantage of genetic algorithm for initialization of neural network weights. The learning is fast, more stable and accurate as compared to back propagation. The system was implemented in Matlab and predicts the risk of heart disease with an accuracy of 89%. [2] Latha Parthiban and R.Subramanian In this paper, a new approach based on coactive neuro-fuzzy inference system (CANFIS) was presented for prediction of heart disease.The proposed CANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach which is then integrated with genetic algorithm to diagnose the presence of the disease. The performances of the CANFIS model were evaluated in terms of training performances and classification accuracies and the results showed that the proposed CANFIS model has great potential in predicting the heart disease. [3] Kavita Rawat, Kavita Burse The proposed paper shows a method performs feature selection and parameters setting in an evolutionary way. The wrapper approach to feature subset selection is used in this paper because of the accuracy. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset without degrading the ANFIS classification accuracy. To verify the effectiveness of the proposed approach, it is tested on ovarian cancer dataset . [4] Dr. Anooj P.K The proposed clinical decision support system for risk prediction of heart patients consists of two phases, (1) automated approach for generation of weighted fuzzy rules, and (2) developing a fuzzy rule-based decision support system. In the first phase, we have used the mining technique, attribute selection and attribute weightage method to obtain the weighted fuzzy rules. Then, the fuzzy system is constructed in accordance with the weighted fuzzy rules and chosen attributes. Finally, the experimentation is carried out on the proposed system using the datasets
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