Abstract
The data mining methods are often beneficial in open domains like business, marketing, and retail. Healthcare is one of these areas that are still in its development phase. The healthcare industry is very rich in information, but sadly, not all data is carefully mined or discovered to draw out the effective, hidden patterns, and due to the lack of helpful analysis tools for discovering hidden relationships and trends in data, decision making is hampered (Dangare and Apte in Int J Comput Appl (0975–888) 47(10), 2012 [1]). There are a lot of advanced techniques in determining the domain that is used to discover knowledge from the healthcare database, particularly in diseases like heart disease, lung cancer, Parkinson’s disease, and others. This paper analyzes the predictions of heart disease on a dataset with 13 attributes. On the heart disease dataset, decision trees, Naive Bayes, support vector machine, linear discriminant analysis, logistic regression, and KNN are evaluated as data mining approaches (classification algorithms). Usually, the algorithms are compared based on their performance measure, and the algorithm with the highest accuracy is implemented on the heart dataset for the further model prediction.
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Sharma, N., Sarkar, D., Dutta, S. (2022). Disease Prediction Using Various Data Mining Techniques. In: Das, K.N., Das, D., Ray, A.K., Suganthan, P.N. (eds) Proceedings of the International Conference on Computational Intelligence and Sustainable Technologies. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6893-7_33
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DOI: https://doi.org/10.1007/978-981-16-6893-7_33
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