Abstract
Heart is a vital organ of the human body. The mortality rate due to cardiovascular or heart disease is in the rising trend. From the review of existing literature, timely diagnosis of the potential heart disease is the key to preventing the risk of early death. But due to the lack of proper health infrastructure in different parts of the country (especially in the rural area), timely diagnosis or the early diagnosis of heart disease has been a problem in Nepal. In this thesis, ensemble learning is used to predict the potential heart risk in patients. First different supervised learning models such as Logistic Regression, SVM, Decision Tree, KNN, and Gaussian Naive Bayes were built out of UCI heart disease dataset. These models gave accuracy of 82.46%, 87.34%, 97.67%, 89.94% and 78.57% respectively. An ensemble model was built by combining aforementioned five supervised ML models. The voting and averaging based ensemble model gave an accuracy of 96.10% and 96.43% respectively. Upon comparison, the ensemble model gave overall better accuracy. A module capable of predicting heart disease for a patient was implemented.
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References
WHO (2017) Country cooperation strategy at a glance. World Health Organization
Murphy SL (2018) Mortality in the United States, 2017. Centers for Disease Control and Prevention
Bhattarai S (2017) Cardiovascular disease trend in Nepal. IJC Heart and Vasculature
Anil OM (2019) Prevalence of cardiovascular risk factors in apparently healthy urban adult population of Kathmandu. J Nepal Health Res Counc 16(41):438–445
Sipai S, Mali D, Shakya S, Mali R (2021) Parkinson’s disease data analysis and prediction using ensemble machine learning techniques. In: Mobile computing and sustainable informatics. Lecture notes on data engineering and communications technologies, vol 68
Shabaz M, Dhiman G, PandeS, Singh P, Bharti R, Khamparia A (2021) Prediction of heart disease using a combination of machine learning and deep learning. In: Computational intelligence and neuroscience, vol 2021
Bharti SK, Shah D, Patel S (2020) Heart disease prediction using machine learning techniques. SN Computer Science
Choubey DK (2020) Heart disease prediction using machine learning and data mining. Int J Recent Technol Eng 9:212–219
Almustafa KM (2020) Prediction of heart disease and classifier’s sensitivity analysis. BMC Bioinform
Theobald O (2017) Machine learning for absolute beginners: a plain English introduction. Scatterplot Press
Borges DM (2021, July) Kdnuggets [Online]. https://www.kdnuggets.com/2019/01/ensemble-learning-5-main-approaches.html
Santarcangelo J (2020) Machine learning with python: a practical introduction. edX course
Chowdary DH (2020) Towards AI [Online]. https://towardsai.net/p/programming/decision-trees-explained-with-a-practical-example-fe47872d3b53
OGIQ (2021) OpengenusIQ [Online]. https://iq.opengenus.org/gaussian-naive-bayes/
UCI (2021) UCI machine learning repository [Online]. https://archive.ics.uci.edu/ml/datasets/heart+disease
Sayed S (2021) An introduction to data science [Online]. https://www.saedsayad.com/decision_tree.htm
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Adhikari, B., Shakya, S. (2022). Heart Disease Prediction Using Ensemble Model. In: Shakya, S., Du, KL., Haoxiang, W. (eds) Proceedings of Second International Conference on Sustainable Expert Systems . Lecture Notes in Networks and Systems, vol 351. Springer, Singapore. https://doi.org/10.1007/978-981-16-7657-4_69
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DOI: https://doi.org/10.1007/978-981-16-7657-4_69
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