Applying machine learning algorithms for the classification of sleep disorders
Siva Rama Raja.s
Computer Science & Engineering Sasi Institute of Technology & Engineering Tadepalligudem, India sivaramaraja@gmail.com
Srisatya.Tadepalli
Computer Science & Engineering Sasi Institute of Technology & Engineering
Tadepalligudem, India
srisatyatadepalli@gmail.co m
Hemalatha.Mudu
Computer Science & Engineering Sasi Institute of Technology & Engineering
Tadepalligudem, India
hemalatha.mudu09@gmail.com
Nikhil.Torlapati
Computer Science & Engineering Sasi Institute of Technology & Engineering
Tadepalligudem, India nikhil123707@gmail.com
Gopi Ramana.Pindra
Computer Science & Engineering Sasi Institute of Technology & Engineering Tadepalligudem, India
gopiramanapindra@gmail.co m
Abstract:
Sleep disorder classification is crucial in improving human quality of life. Sleep disorders and apnoea can have a significant influence on human health. Sleep-stage classification by experts in the field is an arduous task and is prone to human error. The development of accurate
machine learning algorithms (MLAs) for sleep disorder classification requires analysing, monitoring and diagnosing sleep disorders. This paper compares deep learning algorithms and conventional MLAs to classify sleep disorders. This study proposes an optimised method for the Classification of Sleep Disorders and uses the Sleep Health and Lifestyle Dataset publicly available online to
evaluate the proposed model. The optimisations were conducted using a genetic algorithm to tune the parameters of different machine learning algorithms. An evaluation and comparison of the proposed algorithm against
state-of-the-art machine learning algorithms to classify sleep disorders. The dataset includes 400 rows and 13
columns with various features representing sleep and daily activities. The k-nearest neighbours, support vector machine, decision tree, random forest and artificial neural network (ANN) deep learning algorithms were assessed. The experimental results reveal significant performance differences between the evaluated algorithms. The proposed algorithms obtained a classification accuracy of 83.19%, 92.04%, 88.50%, 91.15% and 92.92%,
respectively. The ANN achieved the highest classification accuracy of 92.92%, and its precision, recall and F1-score values on the testing data were 92.01%, 93.80% and 91.93%, respectively. The ANN algorithm achieved higher accuracy than other tested algorithms.
