A Cardiovascular Disease Prediction using Machine Learning Algorithms
Rubini P E1, Deeksha G S2, B Varshaa Shree3, Deepa N4, Abhinav Srivastava5
1Mrs Rubini P E, Assistant Professor, Department of Computer Science Engineering, CMR Institute of Technology, Bangalore (Tamil Nadu), India.
2Deeksha G S, Department of Computer Science Engineering, CMR Institute of Technology, Bangalore (Tamil Nadu), India.
3B Varshaa Shree, Department of Computer Science Engineering, CMR Institute of Technology, Bangalore (Tamil Nadu), India.
4Deepa N, Department of Computer Science Engineering, CMR Institute of Technology, Bangalore (Tamil Nadu), India.
5Abhinav Srivastava, Department of Computer Science Engineering, CMR Institute of Technology, Bangalore (Tamil Nadu), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 491-495 | Volume-8 Issue-5, June 2019 | Retrieval Number: E6996068519/19©BEIESP
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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: Heart Diseases have shown a tremendous hit in this modern age. As doctors deal with precious human life, it is very important for them to be right their results. Thus an application was developed which can predict the vulnerability of heart disease, given basic symptoms like age, gender, pulse rate, resting blood pressure, cholesterol ,fasting blood sugar, resting electrocardiographic results, exercise induced angina,ST depression ST segment the slope at peak exercise, number of major vessels coloured by fluoroscopy and maximum heart rate achieved .This can be used by doctors to re heck and confirm on their patients condition. In the existing surveys they have considered only 10 features for prediction, but in this proposed research work 14 necessary features were taken into consideration. Also, this paper presents a comparative analysis of machine learning techniques like Random Forest (RF), Logistic Regression, Support Vector Machine (SVM), and Naïve Bayes in the classification of cardiovascular disease. By the comparative analysis, machine learning algorithm Random Forest has proven to be the most accurate and reliable algorithm and hence used in the proposed system. This system also provides the relation between diabetes and how much it influences heart disease.
Keywords: Heart Disease; Machine Learning Algorithms; Random Forest; Logistic Regression; Support Vector Machine; Naïve Bayes; Diabetes Influence
Scope of the Article: Machine Learning