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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal of Electrical Engineering and Computer Science
Ramli Musa
International Islamic University Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Depression prediction using machine learning: a review Hanis Diyana Abdul Rahimapandi; Ruhaila Maskat; Ramli Musa; Norizah Ardi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1108-1118

Abstract

Predicting depression can mitigate tragedies. Numerous works have been proposed so far using machine learning algorithms. This paper reviews publications from online electronic databases from 2016 to 2020 that use machine learning techniques to predict depression. The aim of this study is to identify important variables used in depression prediction, recent depression screening tools adopted, and the latest machine learning algorithms used. This understanding provides researchers with the fundamental components essential to predict depression. Fifteen articles were found relevant. We based our review on the systematic mapping study (SMS) method. Three research questions were answered through this review. We discovered that sixteen variables were deemed important by the literature. Not all of the reviewed literature utilizes depression screening tools in the prediction process. Nevertheless, from the five screening tools discovered, the most frequently used were hospital anxiety and depression scale (HADS) and hamilton depression rating scale (HDRS) for general population, while for literature targeting older population geriatric depression scale (GDS) was often employed. A total of twenty-two machine learning algorithms were identified employed to predict depression and random forest was found to be the most reliable algorithm across the publications.
Utilising Target Adjacency Information for Multi-target Prediction Ruhaila Maskat; Ramli Musa; Norizah Ardi; Noor Afni Deraman; Zaaba Ahmad; Qingchen Wang; Shukor Sanim Mohd Fauzi; Ray Adderley JM Gining; Tajul Rosli Razak
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3218

Abstract

In this paper, we explored how information on the cost of misprediction can be used to train supervised learners for multi-target prediction (MTP). In particular, our work uses depression, anxiety and stress severity level prediction as the case study. MTP describes proposals which results require the concurrent prediction of multiple targets. There is an increasing number of practical applications that involve MTP. They include global weather forecasting, social network users’ interaction and the thriving of different species in a single habitat. Recent work in MTP suggests the utilization of “side information” to improve prediction performance. Side information has been used in other areas, such as recommender systems, information retrieval and computer vision. Existing side information includes matrices, rules, feature representations, etc. In this work, we review very recent work on MTP with side information and propose the use of knowledge on the cost of incorrect prediction as side information. We apply this notion in predicting depression, anxiety and stress of 270,322 anonymous respondents to the DASS-21 psychometric scale in Malaysia. Predicting depression, anxiety and stress based on the DASS-21 fit an MTP problem. Often, a patient experiences anxiety as well as depression at the same time. This is not unusual since it has been discovered that both tend to co-exist at different degrees depending on a patient’s experience. By using existing machine learning algorithms to predict the severity levels of each category (i.e., depression, anxiety and stress), the result shows improved precision with the use of cost matrix as side information in MTP.
Detecting candidates of depression, anxiety and stress through malay-written tweets: a preliminary study Muhammad Zahier Nasrudin; Ruhaila Maskat; Ramli Musa
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp787-793

Abstract

Depression, anxiety and stress are not trivial conditions applicable for only the weak-hearted. They can be inflicted by anyone of all age groups, gender, race and social status. While some are courageous to acknowledge their condition, others shy away in shame or denial. In this paper, we proposed a “proactive” approach to detecting candidates of depression, anxiety and stress in an unobtrusive manner by tapping into what Malaysians tweet in Malay language. From this preliminary study, we constructed 165 Malay layman terms which describe depression, anxiety or stress as identified in M-DASS-42 scale. Since Twitter is an informal platform, construction of Malay layman terms is an essential step to the detection of candidates. Our study on 1,789 Malay tweets discovered 6 Twitter users as potential candidates, having high frequency of tweets with any of the layman terms. We can conclude that using tweets can be useful in unobtrusively detecting candidates of depression, anxiety or stress. This paper also identifies open research areas.
Co-Authors Hanis Diyana Abdul Rahimapandi Muhammad Zahier Nasrudin Noor Afni Deraman Norizah Ardi Norizah Ardi Qingchen Wang Ray Adderley JM Gining Ruhaila Maskat Ruhaila Maskat Shukor Sanim Mohd Fauzi Tajul Rosli Razak Zaaba Ahmad