Prediction of Cardiovascular Disease using Machine Learning Algorithms
Muktevi Srivenkatesh

Dr. M. Srivenkatesh*, Associate Professor, Department of Computer Science, GITAM Deemed to be University, Visakhapatnam, Andhra Pradesh, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2404-2414 | Volume-9 Issue-3, February 2020. | Retrieval Number: B3986129219/2020©BEIESP | DOI: 10.35940/ijeat.B3986.029320
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Abstract: Background/Aim: Healthcare is an unavoidable assignment to be done in human life. Cardiovascular sickness is a general class for a scope of infections that are influencing heart and veins. The early strategies for estimating the cardiovascular sicknesses helped in settling on choices about the progressions to have happened in high-chance patients which brought about the decrease of their dangers. Methods: In the proposed research, we have considered informational collection from kaggle and it doesn’t require information pre-handling systems like the expulsion of noise data, evacuation of missing information, filling default esteems if applicable and classification of attributes for prediction and decision making at different levels. The performance of the diagnosis model is obtained by using methods like classification, accuracy, sensitivity and specificity analysis. This paper proposes a prediction model to predict whether a people have a cardiovascular disease or not and to provide an awareness or diagnosis on that. This is done by comparing the accuracies of applying rules to the individual results of Support Vector Machine, Random forest, Naive Bayes classifier and logistic regression on the dataset taken in a region to present an accurate model of predicting cardiovascular disease. Results: The machine learning algorithms under study were able to predict cardiovascular disease in patients with accuracy between 58.71% and 77.06%. Conclusions: It was shown that Logistic Regression has better Accuracy (77.06 %) when compared to different Machine-learning Algorithms.
Keywords: Cardiovascular disease, Machine Learning Algorithms, Performance Evaluators, toxins