A Machine Learning Approach to Mental Disorder Prediction: Handling the Missing Data Challenge

Tsholofelo Mokheleli; Tebogo Bokaba; Tinofirei Museba; Nompumelelo Ntshingila

doi:10.1007/978-3-031-63999-9_6

A Machine Learning Approach to Mental Disorder Prediction: Handling the Missing Data Challenge

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Emerging Technologies for Developing Countries - 6th EAI International Conference, AFRICATEK 2023, Proceedings
Editors	Muthoni Masinde, Sabine Möbs, Antoine Bagula
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	93-106
Number of pages	14
ISBN (Print)	9783031639982
DOIs	https://doi.org/10.1007/978-3-031-63999-9_6
Publication status	Published - 2024
Event	6th EAI International Conference on Emerging Technologies for Developing Countries, AFRICATEK 2023 - Arusha, Tanzania, United Republic of Duration: 11 Dec 2023 → 13 Dec 2023

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	520 LNICST
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	6th EAI International Conference on Emerging Technologies for Developing Countries, AFRICATEK 2023
Country/Territory	Tanzania, United Republic of
City	Arusha
Period	11/12/23 → 13/12/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education
SDG 12 Responsible Consumption and Production

Keywords

Data Imputation
Machine Learning
Mental Disorders Prediction
Missing Values

ASJC Scopus subject areas

Computer Networks and Communications

Access to Document

10.1007/978-3-031-63999-9_6

Cite this

Mokheleli, T., Bokaba, T., Museba, T., & Ntshingila, N. (2024). A Machine Learning Approach to Mental Disorder Prediction: Handling the Missing Data Challenge. In M. Masinde, S. Möbs, & A. Bagula (Eds.), Emerging Technologies for Developing Countries - 6th EAI International Conference, AFRICATEK 2023, Proceedings (pp. 93-106). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 520 LNICST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-63999-9_6

Mokheleli, Tsholofelo ; Bokaba, Tebogo ; Museba, Tinofirei et al. / A Machine Learning Approach to Mental Disorder Prediction : Handling the Missing Data Challenge. Emerging Technologies for Developing Countries - 6th EAI International Conference, AFRICATEK 2023, Proceedings. editor / Muthoni Masinde ; Sabine Möbs ; Antoine Bagula. Springer Science and Business Media Deutschland GmbH, 2024. pp. 93-106 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

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title = "A Machine Learning Approach to Mental Disorder Prediction: Handling the Missing Data Challenge",

abstract = "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{\textquoteright}s potential in mental health analysis and paving the way for further advancements in the field.",

keywords = "Data Imputation, Machine Learning, Mental Disorders Prediction, Missing Values",

author = "Tsholofelo Mokheleli and Tebogo Bokaba and Tinofirei Museba and Nompumelelo Ntshingila",

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Mokheleli, T, Bokaba, T, Museba, T & Ntshingila, N 2024, A Machine Learning Approach to Mental Disorder Prediction: Handling the Missing Data Challenge. in M Masinde, S Möbs & A Bagula (eds), Emerging Technologies for Developing Countries - 6th EAI International Conference, AFRICATEK 2023, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 520 LNICST, Springer Science and Business Media Deutschland GmbH, pp. 93-106, 6th EAI International Conference on Emerging Technologies for Developing Countries, AFRICATEK 2023, Arusha, Tanzania, United Republic of, 11/12/23. https://doi.org/10.1007/978-3-031-63999-9_6

A Machine Learning Approach to Mental Disorder Prediction: Handling the Missing Data Challenge. / Mokheleli, Tsholofelo; Bokaba, Tebogo; Museba, Tinofirei et al.
Emerging Technologies for Developing Countries - 6th EAI International Conference, AFRICATEK 2023, Proceedings. ed. / Muthoni Masinde; Sabine Möbs; Antoine Bagula. Springer Science and Business Media Deutschland GmbH, 2024. p. 93-106 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 520 LNICST).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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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

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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 -

Mokheleli T, Bokaba T, Museba T , Ntshingila N. A Machine Learning Approach to Mental Disorder Prediction: Handling the Missing Data Challenge. In Masinde M, Möbs S, Bagula A, editors, Emerging Technologies for Developing Countries - 6th EAI International Conference, AFRICATEK 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 93-106. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-031-63999-9_6

A Machine Learning Approach to Mental Disorder Prediction: Handling the Missing Data Challenge

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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