Reliability Measurement’s in Depression Detection Using a Data Mining Approach Based on Fuzzy-Genetics

الموضوعات : Journal of Computer & Robotics

Mohammad Nadjafi ¹ , Sepideh Jenabi ² , Adel Najafi ³ , Ghasem Kahe ⁴

1 - Aerospace Research Institute (Ministry of Science, Research and Technology)
2 - Computer engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 - Researcher and Instructure of Computer Science, Tehran, Iran
4 - Aerospace Research Institute (Ministry of Science, Research and Technology), Tehran, P.O.B. 14665-834, Iran

تاريخ الإرسال : 12 الإثنين , جمادى الثانية, 1442 تاريخ التأكيد : 24 السبت , جمادى الثانية, 1442 تاريخ الإصدار : 16 الثلاثاء , ربيع الثاني, 1442

الکلمات المفتاحية: reliability, Feature extraction, Depression disorder, Data Mining Evaluation Metrics, Fuzzy Genetic Algorithm,

ملخص المقالة :

Developing a reliable data mining method is one of the most challenging issues in the features of advanced computer-based systems. Model reliability in depression disorder detection is the determining p-value or confidence limit for accuracy score. In this regard, data mining evaluation metrics provide a path to knowledge discovery and feature extraction is an important process for discovering patterns in data by exploring and modeling big data. The present paper discussed the data mining approach about detection in depression disorder characterized by symptoms such as sadness, feeling empty, anxiety, and sleep symptoms as well as the loss of initiative and interest inactivity. In this survey, a unique dataset containing sensor data collected from patients with depression was used. For each patient, sensor data were measured over several days. In this respect, the represented dataset could be useful for a better understanding of the relationship between depression and motor activity. On the other hand, to overcome the uncertainties raised from wearable sensors (that caused a significant amount of error in similar previous studies using conventional learning methods such as SVM, LR, NB), and also to increase the efficiency and accuracy of the results and to develop a reliable decision-making framework, the evolutionary hybrid machine learning method (fuzzy-genetic algorithm) will be used. The results show the high accuracy of the proposed method compared to other existing methods.

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