Contributing to diagnoses of Mental Disease Using New Optimization Machine Learning Methods (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Mental illness is known to be difficult to diagnose and we can say that if someone has a mentalillness, it can affect someone for years before diagnosis. Geriatricians often encounter a large number ofpatients for treatment without being able to reduce or automatically diagnose them. Designing a system to helpbetter to diagnose this disease and to reduce cost and time is our aim in this study. We used a mental datasetapplied with data mining optimization algorithms and we applied it with the Python programing language,including training the test split and pre-processing feature selection model used random forest (RFE) andfeature importance to enhance the system results and accuracy for mental dataset in our research and processedthe missing values that found with attributes.The best accuracy was achieved by Adaboost optimization model,which gave us 99% accuracy and the Adaboost ensemble merged with the decision tree produced a 94%accuracy. Moreover, the random forest optimization produced better accuracy at 96% and 92% resulted fromusing the SVM algorithm. Finally, this optimization system by merging the two algorithms to work togetherwill be more efficient and better able to help classify and diagnose suffering patients using a huge amount ofdata in little time and at low cost.