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- W2806573048 abstract "A decision support system in healthcare prediction is one of the crucial systems for detection, diagnosis and treatment in its course. Data mining techniques have been widely used to mine knowledgeable information from medical database for research as well as for gaining expertise. Disease prediction is one of the applications where data mining techniques demonstrate successful results, which reduces the efforts on the part of doctors, practitioner, etc. by offering them with data, selected techniques, various experiences-based expertise and a number of cost-effective options of treatment. In this paper, different machine learning algorithms such as gradient boosting model (GBM), XGBoost (XGB) and ensemble models are discussed and have been used to calculate the performances of individual algorithms on a previously selected open-source database. A comparative analysis has been conducted to compare the results obtained. Therefore, in order to maximize the probabilistic output, a combination of algorithms has also been tested as and ensemble model and best result of product of combinations is selected for designing a decision support system in healthcare prediction. The system designed provides an understanding of the efficacy of the system." @default.
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- W2806573048 date "2018-01-01" @default.
- W2806573048 modified "2023-09-27" @default.
- W2806573048 title "A Decision Support System in Healthcare Prediction" @default.
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- W2806573048 doi "https://doi.org/10.1007/978-981-10-8240-5_18" @default.
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