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Erschienen in:
2022 | OriginalPaper | Buchkapitel
Prediction of Liver Patients Using Machine Learning Algorithms
verfasst von : Shefai Tanvir Fayaz, G. S. Tejanmayi, Yerramasetti Kanaka Ruthvi, S. Vijaya Shetty, Sharada U. Shenoy, Guruprasad Bhat
Erschienen in: Emerging Research in Computing, Information, Communication and Applications
Verlag: Springer Singapore
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Abstract
Liver diseases turn out to be lethal when detected late. This creates a dire requirement of an efficient and robust system to defy death with early detection. With machine learning setting to grow its roots in the healthcare industry, it would be a good choice to opt for it to overcome this problem. This study aims to tackle the same by applying selected machine learning algorithms on the Indian Liver Patients Dataset (ILPD), the accuracies of which are finally compared to arrive at the algorithm yielding the best results. For robust results, extensive preprocessing involving sampling, normalization, and particle swarm optimization (PSO) is performed on the data. This proceeds in J48 yielding the best result in classifying patients and non-patients of the dataset. Further, an etiological survey depicting the effect of various attributes on the dataset is presented.