Abstract
This study focuses on the application of classification rule mining techniques to analyse biological and healthcare data, specifically using a tuberculosis dataset. Naive Bayes, Logistic Regression, Decision Tree, Random Forest classifier, K-Nearest Neighbour, and Support Vector Machine were among the classification techniques examined in the investigation. Support Vector Machine (SVM), Random Forest, and Decision Tree algorithms show the highest degree of accuracy.
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Shashikala, D., Rajathi, S., Chandran, C.P., Popat, K. (2024). Classification of Rule Mining for Biomedical and Healthcare Data. In: Rajagopal, S., Popat, K., Meva, D., Bajeja, S. (eds) Advancements in Smart Computing and Information Security. ASCIS 2023. Communications in Computer and Information Science, vol 2038. Springer, Cham. https://doi.org/10.1007/978-3-031-59097-9_1
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DOI: https://doi.org/10.1007/978-3-031-59097-9_1
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