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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Evaluation and Research in Education (IJERE)
Norizah Ardi
Universiti Teknologi MARA
Computer Science & IT Education 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.
Formant characteristics of Malay vowels Izzad Ramli; Nursuriati Jamil; Norizah Ardi
International Journal of Evaluation and Research in Education (IJERE) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (591.121 KB) | DOI: 10.11591/ijere.v9i1.20421

Abstract

The purpose of this study was to investigate and examined the eight vowels formant characteristic of Malay language. Previous research of Malay language only investigated six basic vowels /a/, /e/, /i/, /o/, /u/, /ə/. The vowels /ɔ/, /ε/ that usually exist in a dialect were not included in the previous investigations. In this study, the vowels sound were collected from five men and four women producing the vowels /a/, /e/, /i/, /o/, /u/, /ə/, /ɔ/, /ε/ from different regions and dialects in Malaysia. Formant contours, F1 until F4 of the vowels were measured using interactive editing tool called Praat. Analysis of the formant data showed numerous differences between vowels in terms of average frequencies of F1 and F2, and the degree of overlap among adjacent vowels. When compared with the International Phonetic Alphabet (IPA), most pronunciation of the Malay vowels were at the same position but the vowel /ε/ seen more likely to become a front vowel instead of a central vowel. Consequently, vowel features of the two Malay allophones /ɔ/ and /ε/ were documented and added to the IPA vowel chart. The findings form the fundamental basis for further research of speech synthesis, speech rehabilitation and speech reproduction of the Malay language.
Co-Authors Hanis Diyana Abdul Rahimapandi Izzad Ramli Nursuriati Jamil Ramli Musa Ruhaila Maskat