Predicting Sleep Disorders: Leveraging Sleep Health and Lifestyle Data with Dipper Throated Optimization Algorithm for Feature Selection and Logistic Regression for Classification | ||||
| Computational Journal of Mathematical and Statistical Sciences | ||||
| Volume 3, Issue 2, November 2024, Page 341-358 PDF (422.29 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/cjmss.2024.290167.1053 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
El-Sayed M. El-kenawy   1; Abdelhameed Ibrahim2; Abdelaziz A. Abdelhamid3, 4; Nima Khodadadi5; Laith Abualigah6, 7, 8, 9; Marwa M. Eid10
| ||||
| 1Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology Mansoura, Egypt | ||||
| 2Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt | ||||
| 3Department of Computer Science, College of Computing and Information Technology, Shaqra University, 11961, Shaqra, Saudi Arabi | ||||
| 4Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt | ||||
| 5Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA | ||||
| 6Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan | ||||
| 7Computer Science Department, Al al-Bayt University, Mafraq 25113, Jordan | ||||
| 8MEU Research Unit, Middle East University, Amman 11831, Jordan. | ||||
| 9Applied science research center, Applied science private university, Amman 11931, Jordan | ||||
| 10Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt | ||||
| Abstract | ||||
| This paper is a thorough examination of the modeling of sleep disorders based on machine learning that is applied to the sleep-health-and-lifestyle data. The use of the Dipper Throated Optimization Algorithm for feature selection and Logistic Regression for classification is the basis of the study that explores the effectiveness of predictive models in identifying sleep disorders based on varied sleep metrics and lifestyle factors. The binary Dipper Throated Optimization Algorithm was the most successful with the lowest Average error of 0.71933 uses feature selection as the most effective method, which proves that it is successful the method of choosing the relevant features for predictive modeling. Moreover, Logistic Regression proved to be very efficient in classification; it got an Accuracy of 0.95. The results of these studies support the idea of the personalized treatment and earlier detection of sleep disorders; this, in turn, will be of great help to the progress in sleep health research and healthcare practice. | ||||
| Keywords | ||||
| Sleep health; Lifestyle factors; Feature selection; Dipper Throated Optimization Algorithm; Logistic Regression | ||||
|
Statistics Article View: 540 PDF Download: 581 |
||||
