TY - GEN
T1 - A Machine Learning Approach to Mental Disorder Prediction
T2 - 6th EAI International Conference on Emerging Technologies for Developing Countries, AFRICATEK 2023
AU - Mokheleli, Tsholofelo
AU - Bokaba, Tebogo
AU - Museba, Tinofirei
AU - Ntshingila, Nompumelelo
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.
PY - 2024
Y1 - 2024
N2 - In recent years, the application of Machine Learning (ML) to predict mental disorders has gained significant attention due to its potential for early prediction. This study highlights the challenges of ML in mental disorders prediction, such as missing data in mental health datasets, by comparing four data imputation methods: Mode, Multivariate Imputation by Chained Equations, Hot Deck, and K-Nearest Neighbor (K-NN) to enhance predictive accuracy; and utilizing four ML classifiers and three ensemble methods: Bagging, Boosting, and Stacking, with Mode and K-NN imputation datasets to show consistent performance. The study ultimately contributes to early mental disorder diagnosis and intervention in alignment with the United Nations Sustainable Development Goal 3 (SDG 3) for global health and well-being, by highlighting ML and data imputation’s potential in mental health analysis and paving the way for further advancements in the field.
AB - In recent years, the application of Machine Learning (ML) to predict mental disorders has gained significant attention due to its potential for early prediction. This study highlights the challenges of ML in mental disorders prediction, such as missing data in mental health datasets, by comparing four data imputation methods: Mode, Multivariate Imputation by Chained Equations, Hot Deck, and K-Nearest Neighbor (K-NN) to enhance predictive accuracy; and utilizing four ML classifiers and three ensemble methods: Bagging, Boosting, and Stacking, with Mode and K-NN imputation datasets to show consistent performance. The study ultimately contributes to early mental disorder diagnosis and intervention in alignment with the United Nations Sustainable Development Goal 3 (SDG 3) for global health and well-being, by highlighting ML and data imputation’s potential in mental health analysis and paving the way for further advancements in the field.
KW - Data Imputation
KW - Machine Learning
KW - Mental Disorders Prediction
KW - Missing Values
UR - http://www.scopus.com/inward/record.url?scp=85199601391&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-63999-9_6
DO - 10.1007/978-3-031-63999-9_6
M3 - Conference contribution
AN - SCOPUS:85199601391
SN - 9783031639982
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 93
EP - 106
BT - Emerging Technologies for Developing Countries - 6th EAI International Conference, AFRICATEK 2023, Proceedings
A2 - Masinde, Muthoni
A2 - Möbs, Sabine
A2 - Bagula, Antoine
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 11 December 2023 through 13 December 2023
ER -