Heart Disease Prediction using Machine Learning
J. Gowri1, R. Kamini2, G. Vaishnavi3, S. Thasvin4, C. Vaishna5
1J. Gowri, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India.
2R. Kamini, PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India.
3G. Vaishnavi, PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India.
4S. Thasvin, PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India.
5C. Vaishna, PG Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India.
Manuscript received on 19 June 2022 | Revised Manuscript received on 25 June 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 29-32 | Volume-11 Issue-8, July 2022 | Retrieval Number: 100.1/ijitee.H91480711822 | DOI: 10.35940/ijitee.H9148.0711822
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Abstract: Heart is one most important organ in our body. The prediction of heart disease is most complicated task in today world. There are number of instruments available in today’s worlds. These instruments are so expensive some of them can afford that instrumentals some of them cannot afford the instruments. Early prediction of heart disease will reduce the death rate. we can tell the patients before the hand. In todays world we all have the good amount of data using that good amount of data we can predict the heart disease using various machine learning techniques. The proposed method will tell to patients probabilities of heart diseases. In this paper using the UCI dataset performed various machine learning techniques like Logistic Regression, Decision tree, KNN, Naïve Bayes, Random Forest, XGBoost, Support vector machine . In this paper we used proposed methodology from PHASE I to PHASE VII Using the evaluation metrics we can check the performance of the machine learning which gives more accuracy from the above seven machine learning algorithm..
Keywords: Logistic Regression, Decision tree, KNN, Naïve Bayes, Random Forest, XG Boost, Support vector machine, Accuracy, Machine learning, Prediction, heart.
Scope of the Article: Machine Learning