A Performance Comparison Of Kernel Svm And Hyperparameter Algorithm Using Machine Learning Techniques For Pregnancy Women’s
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Abstract
Objective: To design a framework for the diagnosis of the ANC which will be used to predict, monitor, analyze and forecast the performance measure in the field of ANC in an optimal period, identify the severity of the disease and promote institutional delivery to pregnant women.
Method: In this study, we have used the RCH dataset and using classification model kernel SVM and GridSearchCV Hyperparameter algorithms or applying confusion matrix for accuracy.
Result: In this study based on the dataset in Kernel SVM we have got 85.75% accuracy and our Hyperparameter algorithm GridSearchCV we have got 86% accuracy.
Conclusion: In this study, we have concluded with two algorithms Kernel SVM and GridSearchCV, both the algorithms produced results based on parameters.
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