Prediction and Classification of Low Birth Weight Data Using Machine Learning Techniques

Alfensi Faruk, Endro Setyo Cahyono


Machine learning (ML) is a subject that focuses on the data analysis using various statistical tools and learning processes in order to gain more knowledge from the data. The objective of this research was to apply one of the ML techniques on the low birth weight (LBW) data in Indonesia. This research conducts two ML tasks; including prediction and classification. The binary logistic regression model was firstly employed on the train and the test data. Then; the random approach was also applied to the data set. The results showed that the binary logistic regression had a good performance for prediction; but it was a poor approach for classification. On the other hand; random forest approach has a very good performance for both prediction and classification of the LBW data set


Machine learning; Binary logistic regression; Random forest; Low birth weight

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