A Comparative Study of Various Machine Learning Techniques for Diagnosing Clinical Depression

One of the major areas of machine learning application is in medical diagnosis. Machine learning algorithms can detect patterns in patients' data and generates a diagnosis based on those patterns. There are several machine learning classification algorithms each having different strengths and weaknesses, and this makes it difficult to determine the best one for classification problems. In this paper, machine learning techniques used to classify the clinical depression dataset are Fuzzy Logic, Neural Network, Neuro-Fuzzy System, and Genetic Neuro-Fuzzy System. A total of 134 clinical diagnosis first report depression datasets were used in arriving at prediction. The outcome of the experiment showed that the Genetic Neuro-Fuzzy model generated the best result with a prediction accuracy of 95 %, and cross-validation of 83.2 %. This shows that the model is robust and can make accurate prediction on new, unseen data. This research work will guide future researchers and practitioners to identify new directions for advanced development opportunities in using machine learning in depression diagnosis. It will help policymakers in the area of depression to make informed decisions, especially in the area of best machine learning technique for classification problem related to depression diagnosis. The research is limited to clinical depression diagnosis; future work could be expanded to compute the severity ranks of other depression-connected dysfunctions similar to diabetes, lungs, and cancer diseases.