Mental health diagnosis (Chronic Fatigue Syndrome and depression) using decision tree algorithm, 2025, Zubairi

[Dolphin]

Mental Health Diagnosis (Chronic Fatigue Syndrome and Depression) using Decision Tree Algorithm | G-Tech: Jurnal Teknologi Terapan

ejournal.uniramalang.ac.id

Mental Health Diagnosis (Chronic Fatigue Syndrome and Depression) using Decision Tree Algorithm​

Authors​

• Ach. ZubairiUniversitas Ibrahimy, Indonesia
• Ahmad HomaidiUniversitas Ibrahimy, Indonesia
• Irma YunitaUniversitas Ibrahimy, Indonesia
• Jarot Dwi PrasetyoUniversitas Ibrahimy, Indonesia
• Hermanto HermantoUniversitas Ibrahimy, Indonesia

DOI:​

https://doi.org/10.70609/g-tech.v9i3.7595

Keywords:​

Mental Health, Chronic Fatigue Syndrome, Depression, Decision Tree

Abstract​

Mental health is an important aspect that affects an individual's life, impacting productivity, social relationships and overall quality of life.

The World Health Organization (WHO) states that one in four people worldwide will face mental health challenges.

With the increasing incidence of conditions such as depression and Chronic Fatigue Syndrome (CFS), effective detection and intervention methods are urgently needed.

Data mining, specifically using Decision Tree algorithms, presents a promising approach to address this challenge.

This study utilizes a quantitative methodology to classify depression and CFS patients using a public dataset.

The data collection from Kaggle included variables such as demographics and clinical evaluations, consisting of 1,000 records and 15 predictive attributes.

Data preprocessing addressed noise, specifically missing values, to ensure model accuracy above 80%.

A Decision Tree was implemented, displaying the interpretability of the method by partitioning the data based on the selected attributes.

Evaluation metrics such as accuracy, precision, recall, and F1 score showed accuracy of 99% and precision and recall of 100%.

The results emphasize the potential of the Decision Tree in distinguishing between depression and CFS, enabling early intervention through accurate patient identification.

This study advocates the integration of such machine learning models into clinical practice to improve mental health diagnostics and management, by addressing an important aspect of public health.

 

[Yann04]

Not sure why they ran a model to get this? Seems like a pretty intuitive decision graph.

 

[Utsikt]

I feel bad for the Indonesians if ME/CFS is classified as a mental health disorder..

They used a synthetic data set to create a decision tree.

The tree says that if you’ve got PEM, you’ve definitively got ME/CFS. Depending on your PHQ9 score, you might also have depression.

The exercise frequency logic looks completely nonsensical to me, and would have no real life value.

Other than that, we know how flawed the questionnaires are and how prone they are to false positives in sick people, so they are probably not suitable for real life either.

Which leaves us with only the first question about the presence of PEM, but that isn’t much of a decision tree. It seems like they have not understood that you can’t have ME/CFS without PEM, and that in a sample with only pwME/CFS and pwDepression (or both) everyone with PEM will have ME/CFS.

This weird paragraph is also included:

Figure 4 provides a visual view of how the model makes decisions based on pruning the attributes in the data. I am excited about the potential of utilizing this decision tree image to communicate the model results with the team and other stakeholders. Furthermore, the model can be used to predict depression and CFS status in new patients. Using this model, medical personnel can quickly and accurately assess the patient's condition and plan appropriate interventions. This shows the great potential of the Decision Tree algorithm in the field of mental health, especially in the context of diagnosis and management of CFS and depression.