Data Wrangle and Kurtosis Matching Regression Based Machine Learning for Maternal Health Risk Prediction
Keywords:
Machine Learning, Maternal Health Risk Prediction, Scale Normalization, Data Wrangling, Weight Jarque–Bera, Kurtosis Matching RegressionAbstract
Prediction of health disease employing ML algorithm system using predicting a patient’s illness based on observed symptoms. Proposed Scale Normalized Data Wrangle and Kurtosis Matching Regression (SNDW-KMR) is introduced for maternal health risk prediction. Initially, pregnancy risk factor dataset is considered as input. Robust Scaled Normalization Process and Box Plot Data Wrangling are performed for normalization and outlier detection. Features necessitated for pregnancy health risk prediction is selected employing Weight Jarque–Bera Kurtosis Matching Regressive Feature Selection algorithm. Experimental evaluation conducted with several metrics
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