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Cardiovascular Disease Prediction using Ensemble Classification Algorithm in Machine Learning


Affiliations
1 Department of Computer Science, St. Xavier's College, India
2 Department of Computer Science, Government General Degree College, India
 

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Cardiovascular disease includes a wide range of heart-related illnesses and has surpassed cancer as the top cause of mortality worldwide in recent decades. Many people nowadays are engrossed in their daily lives and engage in various activities while ignoring their health. As a result of their rushed lifestyles and disrespect for their health, the number of people becoming unwell is increasing every day. According to the World Health Organization, heart disease claims the lives of over 31% of the world's population. As a result, doctors must be able to predict whether a patient may develop heart illness, but the amount of data collected by the medical sector or hospitals, on the other hand, is so vast that it can be difficult to analyze at times. This research paper assessed several aspects of heart illness and develops a model based on supervised learning methods like Gaussian Naïve Bayes and AdaBoosting algorithm. The purpose of this research is to figure out how to anticipate whether a patient will develop heart disease. The AdaBoosting algorithm achieves a great accuracy score of 95%, according to the data.

Keywords

Heart Disease Prediction, GaussianNB, Machine Learning, Adaboosting Algorithm, Healthcare
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  • V. Ramalingam, A. Dandapath and M. Karthik Raja, “Heart Disease Prediction using Machine Learning Techniques: A Survey”, International Journal of Engineering and Technology, Vol. 7, pp. 684-687, 2018.
  • World Health Organization, “Global Atlas on Cardiovascular Disease Prevention and Control”, Available at https://apps.who.int/iris/handle/10665/44701, Accessed at 2011.
  • M. Gandhi and S.N. Singh, “Predictions in Heart Disease using Techniques of Data Mining”, Proceedings of International Conference on Futuristic Trends on Computational Analysis and Knowledge Management, pp. 25-27, 2015.
  • S. Palaniappan and R. Awang, “Intelligent Heart Disease Prediction System using Data Mining Techniques”, Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, pp. 108-115, 2008.
  • J. Thomas and R.T. Princy, “Human Heart Disease Prediction System using Data Mining Techniques”, Proceedings of International Conference on Circuit, Power, and Computing Technologies, pp. 1-13, 2016.
  • S. Indhumathi and G. Vijaybaskar, “Web-Based Health Care Detection using Naive Bayes Algorithm”, Proceedings of International Conference on Futuristic Trends on Computational Analysis, pp. 1-12, 2015.
  • K. Pahwa and R. Kumar, “Prediction of Heart Disease using Hybrid Technique for Selecting Features”, Proceedings of International Conference on Electrical, Computer and Electronics, pp. 500-504, 2017.
  • S. Xu, Z. Zhang and T. Zhu, “Cardiovascular Risk Prediction Method based on CFS Subset Evaluation and Random Forest Classification Framework”, Proceedings of International Conference on Big Data Analysis, pp. 228-232, 2017.
  • S. Rajathi and G. Radhamani, “Prediction and Analysis of Rheumatic Heart Disease using kNN Classification with ACO”, Proceedings of International Conference on Data Mining and Advanced Computing, pp. 68-73, 2016.
  • R. Saini, N. Bindal and P. Bansal P, “Classification of Heart Diseases from ECG Signals using Wavelet Transform and kNN Classifier”, Proceedings of International Conference on Futuristic Trends on Computing, Communication and Automation, pp. 1208-1215, 2015.
  • M.A. Jabbar, B.L. Deekshatulu and P. Chandra P, “Alternating Decision Trees for Early Diagnosis of Heart Disease”, Proceedings of International Conference on Circuits, Communication, Control and Computing, pp. 322-328, 2014.
  • Heart Disease Data Set, Available at: https://archive.ics.uci.edu/ml/datasets/Heart+Disease, Accessed at 2021.
  • Ping Cao, Bailu Ye, Linghui Yang and Qing Pan, “Preprocessing Unevenly Sampled RR Interval Signals to Enhance Estimation of Heart Rate Deceleration and Acceleration Capacities in Discriminating Chronic Heart Failure Patients from Healthy Controls”, Computational and Mathematical Methods in Medicine, Vol. 2020, pp. 1-14, 2020.
  • H. A. Esfahani and M. Ghazanfari, ‘‘Cardiovascular Disease Detection using a New Ensemble Classifier”, Proceedings of International Conference on Futuristic Trends on Computational Analysis and Knowledge Management, pp. 1011-1014, 2014.
  • T. Vivekanandan and N.C.S.N. Iyengar, “Optimal Feature Selection using a Modified Differential Evolution Algorithm and its Effectiveness for Prediction of Heart Disease”, Computers in Biology and Medicine, Vol. 90, pp. 125-136, 2017.
  • M. Sai Shekhar, Y. Mani Chand and L. Mary Gladence, “Heart Disease Prediction using Machine Learning”, Lecture Notes in Electrical Engineering, Vol. 708, No. 11, pp. 603-609, 2021.

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  • Cardiovascular Disease Prediction using Ensemble Classification Algorithm in Machine Learning

Abstract Views: 100  |  PDF Views: 22

Authors

Rajarshi Sinha Roy
Department of Computer Science, St. Xavier's College, India
Anupam Sen
Department of Computer Science, Government General Degree College, India

Abstract


Cardiovascular disease includes a wide range of heart-related illnesses and has surpassed cancer as the top cause of mortality worldwide in recent decades. Many people nowadays are engrossed in their daily lives and engage in various activities while ignoring their health. As a result of their rushed lifestyles and disrespect for their health, the number of people becoming unwell is increasing every day. According to the World Health Organization, heart disease claims the lives of over 31% of the world's population. As a result, doctors must be able to predict whether a patient may develop heart illness, but the amount of data collected by the medical sector or hospitals, on the other hand, is so vast that it can be difficult to analyze at times. This research paper assessed several aspects of heart illness and develops a model based on supervised learning methods like Gaussian Naïve Bayes and AdaBoosting algorithm. The purpose of this research is to figure out how to anticipate whether a patient will develop heart disease. The AdaBoosting algorithm achieves a great accuracy score of 95%, according to the data.

Keywords


Heart Disease Prediction, GaussianNB, Machine Learning, Adaboosting Algorithm, Healthcare

References