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• W4311184466 abstract "Gestational period is a significant factor in determining the well-being and survival of the infant. An abnormal gestational period has adverse effects on the mother and fetus, which might even lead to mortality. Machine learning offers the solution by analyzing the maternal and fetal attributes and predicting the number of gestational days ahead of time. In this study, we develop various machine learning models, to determine the gestational days by analyzing the maternal height, weight, age, and smoking status. Linear regression, ridge regression, robust regression, lasso regression, elastic net regression, polynomial regression, stochastic gradient descent, random forest regressor, SVM regressor, and ANN models were utilized. The models were evaluated based on the statistical parameters, such as MAE, RMSE, R2 score, and explained variance score. The results reveal that the linear regression model outperformed the others by achieving a minimal MAE and RMSE scores of 8.658248 and 11.801136, respectively, and a higher R2 value of 0.010901 and explained variance score of 0.021675." @default.
• W4311184466 created "2022-12-24" @default.
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• W4311184466 date "2022-12-03" @default.
• W4311184466 modified "2023-09-27" @default.
• W4311184466 title "Predicting the Gestational Period Using Machine Learning Algorithms" @default.
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• W4311184466 doi "https://doi.org/10.1007/978-981-19-6004-8_44" @default.
• W4311184466 hasPublicationYear "2022" @default.
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