Comparative Analysis of Machine Learning Classification Techniques for Neonatal Postprandial Hypoglycemia Symptoms Screening.

Abstract

Neonatal postprandial hypoglycaemia occurs when blood sugar level (BSL) is too low to cause symptoms of impaired brain function among new-born babies. Machine learning algorithms such as Neural Networks, SVM, Naive Bayes, Decision Tree are widely used for detection and classification process of the disease. The Objective of this study is to design a model which shall compare the performance of three machine learning classification algorithms namely Decision Tree, SVM and Naive Bayes to detect diabetes at an early stage. The performances of all the three algorithms are evaluated on various measures such as accuracy, Recall, Precision and F-Measure. Classified instances are used to measure Accuracy. The results show that Naive Bayes outperforms with the highest accuracy of 86.40% comparatively other algorithms. This work forms basis for our next step which is utilizing Naïve Bayes Algorithm and Artificial Neural Network (ANN) for Type 1 Diabetes disease treatment.