Titlebook: Computational Intelligence in Healthcare Informatics; D. P. Acharjya,Kun Ma Book 2024 The Editor(s) (if applicable) and The Author(s), und

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Strategien an den Devisenmärktendifferent performance matrices like accuracy, precision, and recall. A comparative study of individual results of the models like support vector machine, K-nearest neighbor, Naïve Bayes, decision tree, random forest, and artificial neural network for predicting cardiovascular disease is carried out.

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Refining Metabolic Network by Fuzzy Matching of Metabolite Names for Improving Metabolites Ranking Tames from the two models so that the computational complexity of SLWBMISM can be reduced. After obtaining the identical names of metabolites, two models have been merged without duplication, and metabolite similarity is obtained using SLWBMISM. Then the process of reconstructing the metabolic networ

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Diabetes Prediction: A Comparison Between Generalized Linear Model and Machine Learningumber 1 represents the existence of diabetes, whereas the value 0 represents the absence of diabetes. This chapter compares the results of classification algorithms, viz., binary logistic regression model and a support vector machine to detect diabetes early. The models are evaluated using the accur

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Localization and Classification of Brain Tumor Using Multi-layer Perceptronr kinds. Based on the examination of sizable datasets, this study presents a strategy for identifying various tumor forms of the brain. The precise structure of the brain may be seen in MRI pictures and thus taken into consideration for analysis without the need for surgery. The MRI scan reveals the

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