Titlebook: Nature-Inspired Computation in Data Mining and Machine Learning; Xin-She Yang,Xing-Shi He Book 2020 Springer Nature Switzerland AG 2020 Na

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Performance-Based Prediction of Chronic Kidney Disease Using Machine Learning for High-Risk Cardiovgistic regression (Ridge and Lasso), neural network (logistic and stochastic gradient descent), and support vector machine (Radial Basis Function and Polynomial) had very high accuracies and efficiency. With an efficiency of 93.4% and a classification accuracy of 91.7%, Polynomial Support Vector Mac

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ricas, and to discuss the alternatives available to improve the existing and future water quality conditions in a cost-effective and timely manner, the Third World Centre for Water M- agement in Mexico, the Nat978-3-642-06354-1978-3-540-30444-9Series ISSN 1614-810X Series E-ISSN 2198-316X