Usage of Data Mining Techniques in Predicting the Heart Diseases Decision Tree & Random Forest Algorithm
G S Mallikarjuna Rao1, K Anitha2
1GS Mallikarjuna Rao*, Department of Computer Applications,Gayatri Vidya Parishad College of Engineering(Autonomous),Madhurawada, Visakhapatnam, Andhra Pradesh, India.
2K Anitha, Department of Computer Applications, Gayatri Vidya Parishad College of Engineering (Autonomous), Madhurawada, Visakhapatnam, Andhra Pradesh, India.
Manuscript received on November 16, 2019. | Revised Manuscript received on 23 November, 2019. | Manuscript published on December 10, 2019. | PP: 963-967 | Volume-9 Issue-2, December 2019. | Retrieval Number: H7168068819/2019©BEIESP | DOI: 10.35940/ijitee.H7168.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nowadays, heart disease is the main cause of several deaths among all other diseases. Due to the lack of resources in the medical field, the prediction of heart diseases becomes a major problem. For early diagnosis and treatment, some classification algorithms such as Decision Tree and Random Forest Algorithm are used. The data mining techniques compare the accuracy of the algorithm and predict heart diseases. The main aim of this paper is to predict heart disease based on the dataset values. In this paper we are comparing the accuracy of above two algorithms. To implement these methods the following steps are used. In first phase, a dataset of 13 attributes is collected and it was applied on classification techniques using the Decision tree and Random Forest Algorithms. Finally, the accuracy is collected for both the algorithms. In this paper we observed that random forest is generating better results than decision tree in prediction of heart diseases.
Keywords: Classification, Decision Tree, Heart disease, Random Forest.
Scope of the Article: Classification