Abstract
Machine learning, as we know, is a broad application of artificial intelligence (AI) which gives the capability to the machine to learn automatically and improve itself by own experience. Machine learning is doing excellent job in the medical field. As diabetes is one of the vital diseases which is basically a silent killer, it needs to be more addressed and detected or predicted as well. In this present work, several machine learning algorithms have been used to detect it. We are using various classification and prediction method to detect the diseases more accurately in real time. We have compared all the classification models like logistic regression, linear discriminant analysis, K-Nearest neighbor, decision tree, Naive Bayes, support vector machine to get the more accurate results. Univariate feature selection method also has been used and our new feature selection method has been developed. How this new feature selection method is working and how it over-performs the other feature selection methods has been shown in this work.
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Ghosh, S., Rakshit, P., Paul, S., Sen, R., Manna, R., Shaw, S. (2022). Comparative Assessment of Different Feature Selection Methods with Proposed Method in the Application of Diabetes Detection. In: Sarma, H.K.D., Balas, V.E., Bhuyan, B., Dutta, N. (eds) Contemporary Issues in Communication, Cloud and Big Data Analytics. Lecture Notes in Networks and Systems, vol 281. Springer, Singapore. https://doi.org/10.1007/978-981-16-4244-9_38
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DOI: https://doi.org/10.1007/978-981-16-4244-9_38
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