Heart Disease Prediction using Different Machine Learning Algorithms
Article Sidebar
Main Article Content
Abstract
Identifying a person's potential for developing heart disease is one of the most challenging tasks medical professionals faces today. With nearly one death from heart disease every minute, it is the leading cause of death in the modern era [4]. The database is taken from Kaggle. Various machine learning algorithms are used for prediction of heart disease detection here are Random Forest, XG-Boost, K- Nearest Neighbors (KNN), Logistic Regression, Support Vector Machines (SVM). All these algorithms are implemented using Python programming with Google collab. The performance evaluation parameters used here are Accuracy, precision, recall and Fi-score. Training and testing are implemented for different ratios such as 60:40, 70:30 and 80:20. From the analysis and comparisons of evaluation parameters of all the above algorithms, XG-Boost is having the highest accuracy and recall value. KNN having worst accuracy and recall amongst all. XG-Boost is having a training accuracy of 98.86, 98.74 and 97.68 for training and testing ratio of 60:40, 70:30 and 80:20 respectively. XG-Boost is having a testing accuracy of 95.85, 95.45 and 96.09 for training and testing ratio of 60:40, 70:30 and 80:20 respectively. So, XG-Boost algorithm can be used for obtaining the best prediction for heart disease. This type of heart disease prediction can be used as a secondary diagnostic tool for doctors, for best and fast prediction. This can help the early prediction of heart disease thus increasing the chances of the saving the life heart patient.
Article Details
References
Nagaraj M Lutimath, Chethan C, Basavaraj S Pol.,’ Prediction of Heart Disease using Machine Learning’, International journal Of Recent Technology and Engineering,8,(2S10), pp 474-477, 2019.
Kavitha, B. S., & Siddappa, M., A Survey on Machine Learning Techniques to Predict Heart Disease, International Journal of Computer Science & Communication (ISSN: 0973-7391) Special Issue, Page 48-53, December 2020
Dr. Poonam Ghuli, Heart Disease Prediction using Machine Learning, International Journal of Engineering Research & Technology ISSN: 2278-0181IJERTV9IS040614 (IJERT), Vol. 9 Issue 04, April-2020.
Rishabh Magar, Rohan Memane, Suraj Raut, Heart disease prediction using machine learning,2020 JETIR, Volume 7, Issue 6, June 2020
A. H. Fauzi, A. F. Malik, and M. S. Rizal, "Medical Insurance Cost Prediction using Random Forest and Decision Tree," 2020 International Conference on Data and Software Engineering (ICoDSE), Yogyakarta,
Nair, K., Motagi, N., Narayankar, R., Rajani, P.K. (2023). Error Detection and Error Concealment of Medical Images Using Frequency Selective Extrapolation (FSE) Algorithm. In: Kaiser, M.S., Xie, J., Rathore, V.S. (eds) ICTCS2021, Lecture Notes in Networks and Systems, vol 401, pp 495–504, Springer, Singapore.
Al-Fahoum, A. S., Al-Hazaimeh, H., & Al-Madi, N. (2020). An intelligent system for heart disease prediction based on machine learning. IEEE Access, 8, 27726-27738.
Fajr Ibrahem Alarsan., and Mamoon Younes ‘Analysis and classification of heart diseases using heartbeat features and machine learning algorithms’,Journal Of Big Data,2019;6:81.
Jayesh Kolhe, Guruprasad Deshpande, Gargi Patel, Rajani P.K, “Crop Decision using Various Machine Learning Classification Algorithms”, published in conference proceedings by Springer SIST. ISSN Number - 2190-3018,Smart Innovation, Systems and Technologies, 312, pp. 495–502, 2023 https://link.springer.com/chapter/10.1007/978-981-19-3575-6_49
R. Gupta and S. Yusuf, “Challenges in management and prevention of ischemic heart disease in low socioeconomic status people in LLMICs,” BMC Medicine, vol. 17, no. 1, p. 209, 2019.
T.Nagamani, S.Logeswari, B.Gomathy,Heart Disease Prediction using Data Mining with Mapreduce Algorithm,International Journal of Innovative Technology and Exploring Engineering (IJITEE)
ISSN: 2278-3075, Volume-8 Issue-3, January 2019
Karim, M. A., & Majumdar, S. (2019). Diagnosis of heart disease using machine learning algorithms: A survey. Journal of Ambient Intelligence and Humanized Computing, 10(5), 1845-1867.
Avinash Golande, Pavan Kumar T,International Journal of Recent Technology and Engineering (IJRTE)
ISSN: 2277-3878, Volume-8, Issue-1S4, June 2019
P. Umasankar and V. ,iagarasu, “Decision support system for heart disease diagnosis using interval vague set and fuzzy association rule mining,” in Proceedings of the International Conference on Devices, Circuits and Systems (ICDCS),pp. 223–227, Coimbatore, India, March 2018.
Mr. Chala Beyene, Prof. Pooja Kamat, “Survey on Prediction and Analysis of the Occurrence of Heart Disease Using Data Mining Techniques”, International Journal of Pure And Applied Mathematics, (2018).
Rajesh N, T Maneesha, Shaik Hafeez and Hari Krishna, "Prediction of Heart Disease Using Machine Learning Algorithms," International Journal of Engineering & Technology, vol. 7, pp. 364-366, 2018.
Gavhane, A., Kokkula, G., Pandya, I., & Devadkar, K. (2018, March). Prediction of heart disease using machine learning. In 2018 Second International Conference on Electronics,Communication and Aerospace Technology (ICECA) (pp. 1275-1278). IEEE.
.N. Khateeb and M. Usman, “Efficient heart disease prediction system using k-nearest neighbor classification technique,” in Proceedings of the International Conference on Big Data and Internet of Fing (BDIOT), pp. 21–26, ACM, London UK, December 2017.
Sharma, H., & Rizvi, M. A. (2017). Prediction of heart disease using machine learning algorithms: A survey. International Journal on Recent and Innovation Trends In Computing and Communication, 5(8),99-104.
V. Krishnaiah, G. Narasimha, N. Subhash Chandra, “Heart Disease Prediction System using
Data Mining Techniques and Intelligent Fuzzy Approach: A Review” IJCA 2016.
Thomas, J., & Princy, R. T. (2016, March). Human heart disease prediction system using data mining techniques. In 2016 international conference on circuit, power and computing technologies (ICCPCT).
K.Sudhakar, Dr. M. Manimekalai “Study of Heart Disease Prediction using Data Mining”, IJARCSSE
Mohammad Taha Khan, Dr. Shamimul Qamar and Laurent F. Massin, “A Prototype of Cancer/Heart Disease Prediction Model Using Data Mining”, (IJAER), 2012.
Linkfor dataset:https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset