Enhancing Depression Detection Accuracy in Northwest Nigerian Adults Using Ensemble Learning Technique | IJCT Volume 13 – Issue 4 | IJCT-V13I4P13
International Journal of Computer Techniques
ISSN 2394-2231
Author
Mohammed Ali Kawo, Shuaibu Samaila
Abstract
Millions of individuals all over the world suffer from depression, a serious and common mental illness. In Northwest Nigeria, depression is still not well recognized because of stigma, the use of multiple languages, including English, Hausa, and Fulfulde, and a lack of mental health resources for thorough detection. In order to increase accuracy and equality across language groups, this study suggests an ensemble-based detection architecture that incorporates complementary classifiers trained on clinical screening datasets. Researchers have used artificial intelligence (AI) to automatically detect depression symptoms. Majority of healthcare researchers and management use series of machine learning techniques to improve disease detection, diagnosis, and prediction in order to aid in the decision-making. This study investigates how well machine learning ensemble techniques can improve the precision of depression detection in northwest Nigeria. Four individual base machine learning algorithms such as Randon Forest, Decision Tree, Support Vector Machine and K-Nearest Neighbors will be used as a pipeline combination of the classifiers to form Stack Ensemble technique that will be trained on clinical screened dataset from Primary Health Care Centers to enhance depression detection. The entire model will be evaluated using the appropriate machine learning metrics such as accuracy, precision, F1-score, recall and ROC-AUC respectively.
Keywords
Stack Ensemble, Machine learning, Depression, Support Vector Machine, Naïve Bayes.
Conclusion
In this research, stack ensemble method was used to enhance depression detection with sample data collected from local primary healthcare centers within the various states, through the utilization of machine learning classifiers such as Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN) and Decision Tree (DT) to form a Meta-Stack Ensemble classifier. The performance of the results in this research donate to the decision that stack ensemble model is a benchmark to enhance depression detection in Northwestern Nigeria. This research is significant for several advancements which includes:
a. Recognizing and solving the traditional processes of detecting depression in northwestern Nigeria.
b. Development of the Stack Ensemble machine learning technique to enhancing depression detection Accuracy in Northwest Nigerian Adults.
c. It compares the ensemble technique with individual base classifiers to determine the most effective method.
In summary, this research captions an extensive improvement in our knowledge and response to an enhanced detection depression in Northwest Nigeria. It highlights the importance of merging trending analytical methods to health research. To efficiently enhance the detection of depression, it is authoritative to adapt these findings into practicable policy and endlessly improving the approaches based on current datasets to inspire multifaceted collaboration.
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How to Cite This Paper
Mohammed Ali Kawo, Shuaibu Samaila (2026). Enhancing Depression Detection Accuracy in Northwest Nigerian Adults Using Ensemble Learning Technique. International Journal of Computer Techniques, 13(4). ISSN: 2394-2231.
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