Comparative Study of Various Machine Learning Algorithms for Prediction of Insomnia

Abstract

An early diagnosis of insomnia can prevent further medical aids such as anger issues, heart diseases, anxiety, depression, and hypertension. Fifteen machine learning algorithms have been applied and 14 leading factors have been taken into consideration for predicting insomnia. Seven performance parameters (accuracy, kappa, the true positive rate, false positive rate, precision, f-measure, and AUC) are used and for implementation. The authors have used python language. The support vector machine is giving higher performance out of all algorithms giving accuracy 91.6%, f-measure is 92.13, and kappa is 0.83. Further, SVM is applied on another dataset of 100 patients and giving accuracy 92%. In addition, an analysis of the variable importance of CART, C5.0, decision tree, random forest, adaptive boost, and XG boost is calculated. The analysis shows that insomnia primarily depends on the factors, which are the vision problem, mobility problem, and sleep disorder. This chapter mainly finds the usages and effectiveness of machine learning algorithms in Insomnia diseases prediction.