Depression has been the largest mental health problem affecting the public health. Early detection of persons suffering from depression is crucial for effective mitigation and treatment. The key to this can only be achieved when clear symptoms of depression are used to detect patients’ depression conditions. The objective of this study is to develop a predictive model for depression that uses the symptoms. The study used both simulated data and real data from the hospitals. The study developed hidden markov model that help to compute the transitional probabilities. The study also used the logistic regression to assess the predictive power of the symptoms of depression. The study found that insomnia positively influence the probability of depression among the patients. The study also found that guilt positively influence the probability of depression among the patients. From the results, the study found that suicidal positively influence the probability of depression among the patients and also fatigue influence the probability of depression. From the study it was also found that retardation positively influence the probability of depression. Finally, found that the change in anxiety negatively influence the probability of depression among the patients. The study also conclude that the predictive model can be used to predict the depression status of the patients by a medical doctor given that the observable symptoms are present.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 14, Issue 1) |
| DOI | 10.11648/j.ajtas.20251401.11 |
| Page(s) | 1-11 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Depression, Transition Probability, Hidden Markov, Logistic
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APA Style
Mwangi, C., Nyongesa, K., Odero, E. A. (2025). Predictive Model for Depression Without Medical Intervention. American Journal of Theoretical and Applied Statistics, 14(1), 1-11. https://doi.org/10.11648/j.ajtas.20251401.11
ACS Style
Mwangi, C.; Nyongesa, K.; Odero, E. A. Predictive Model for Depression Without Medical Intervention. Am. J. Theor. Appl. Stat. 2025, 14(1), 1-11. doi: 10.11648/j.ajtas.20251401.11
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Download@article{10.11648/j.ajtas.20251401.11,
author = {Charles Mwangi and Kennedy Nyongesa and Everlyne Akoth Odero},
title = {Predictive Model for Depression Without Medical Intervention},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {14},
number = {1},
pages = {1-11},
doi = {10.11648/j.ajtas.20251401.11},
url = {https://doi.org/10.11648/j.ajtas.20251401.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20251401.11},
abstract = {Depression has been the largest mental health problem affecting the public health. Early detection of persons suffering from depression is crucial for effective mitigation and treatment. The key to this can only be achieved when clear symptoms of depression are used to detect patients’ depression conditions. The objective of this study is to develop a predictive model for depression that uses the symptoms. The study used both simulated data and real data from the hospitals. The study developed hidden markov model that help to compute the transitional probabilities. The study also used the logistic regression to assess the predictive power of the symptoms of depression. The study found that insomnia positively influence the probability of depression among the patients. The study also found that guilt positively influence the probability of depression among the patients. From the results, the study found that suicidal positively influence the probability of depression among the patients and also fatigue influence the probability of depression. From the study it was also found that retardation positively influence the probability of depression. Finally, found that the change in anxiety negatively influence the probability of depression among the patients. The study also conclude that the predictive model can be used to predict the depression status of the patients by a medical doctor given that the observable symptoms are present.},
year = {2025}
}
TY - JOUR T1 - Predictive Model for Depression Without Medical Intervention AU - Charles Mwangi AU - Kennedy Nyongesa AU - Everlyne Akoth Odero Y1 - 2025/01/07 PY - 2025 N1 - https://doi.org/10.11648/j.ajtas.20251401.11 DO - 10.11648/j.ajtas.20251401.11 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 1 EP - 11 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20251401.11 AB - Depression has been the largest mental health problem affecting the public health. Early detection of persons suffering from depression is crucial for effective mitigation and treatment. The key to this can only be achieved when clear symptoms of depression are used to detect patients’ depression conditions. The objective of this study is to develop a predictive model for depression that uses the symptoms. The study used both simulated data and real data from the hospitals. The study developed hidden markov model that help to compute the transitional probabilities. The study also used the logistic regression to assess the predictive power of the symptoms of depression. The study found that insomnia positively influence the probability of depression among the patients. The study also found that guilt positively influence the probability of depression among the patients. From the results, the study found that suicidal positively influence the probability of depression among the patients and also fatigue influence the probability of depression. From the study it was also found that retardation positively influence the probability of depression. Finally, found that the change in anxiety negatively influence the probability of depression among the patients. The study also conclude that the predictive model can be used to predict the depression status of the patients by a medical doctor given that the observable symptoms are present. VL - 14 IS - 1 ER -
Department of Mathematics and Actuarial Science, Kabarak University, Nakuru, Kenya
Department of Mathematics, Maside Muliro University, Kakamega, Kenya
Department of Mathematics, Maside Muliro University, Kakamega, Kenya
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