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

Subject Areas : 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

Received: 2021-01-25 Accepted : 2021-02-06 Published : 2020-12-01

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

Abstract :

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.

References:

[1] Depression: Overview, http://www.WHO.int/newsroom /factsheets/detail/depression.
[2] Susel G. A & Isabel de la T. & Sofiane H. & Miguel L.-C. & Diego C. B. & Lola M. N. & Manuel F. “Data Mining Algorithms and Techniques in Mental Health: A Systematic Review” Journal of Medical Systems (2018) 42: 161 https://doi.org/10.1007/s10916-018-1018-2
[3] Shahrzad Oveisi, Mohammad Ali Farsi, Mohammad Nadjafi, Ali Moeini “A New Approach to Promote Safety in the Software Life Cycle”, Journal of Computer & Robotics 12 (1), 2019 77-91
[4]Golriz Amooee, Behrouz Minaei-Bidgoli, Malihe Bagheri-Dehnavi, “A Comparison Between Data Mining Prediction Algorithms for Fault Detection (Case study: Ahanpishegan co.)” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 6, No 3, November 2011 ISSN 1694-0814
[5] McCue, C. Data Mining and Predictive Analytics. Data Mining and Predictive Analysis, 31–48. doi:10.1016/b978-0-12-800229-2.00003-1, 2015.
[6] Larose, Daniel T., and Chantal D. Larose, Discovering knowledge in data: an introduction to data mining. Vol. 4. John Wiley & Sons, 2014.
[7] Susel G. A & Isabel de la T. & Sofiane H. & Miguel L.-C. & Diego C. B. & Lola M. N. & Manuel F. “Data Mining Algorithms and Techniques in Mental Health: A Systematic Review” Journal of Medical Systems (2018) 42: 161 https://doi.org/10.1007/s10916-018-1018-2
[8] Yang, S., Zhou. P., Duan, K., Hossain, M. S., and Alhamid, M. F., "emHealth: Towards emotion health through depression prediction and intelligent health recommender system". Mob. Netw. Appl.; 1-11, 2017.

[9] Jena, L., and Kamila, N. K., "A model for prediction of human depression using Apriori algorithm". 2014 Int. Conf Inf Technol.; 240-244, 2014.
[10] Jung, Y., and Yoon, Y. I., "Multi-level assessment model for wellness service based on human mental stress level". Mu/timed. Tools Appl. 76(9):11305-11317, 2017.
[11] Morales, S., Barros, J., Echavarri, 0., Garcia, F., Osses, A., Moya, C. et al., "Acute mental discomfort associated with suicide behavior in a clinical sample of patients with affective disorders: Ascertaining critical variables using artificial intelligence tools". Front. Psychiatr: 8:7, 2017.
[12] Mohammadi, M., Al-Azab, F., Raahemi, B., Richards, G., Jaworska, N., Smith, D. et al., "Data mining EEG signals in depres sion for their diagnostic value". BMC Med. Inform. Decis. Mak. 15(1):108, 2015.
[13] J. F. Dipnall, J. A. Pasco, M. Berk, L. J. Williams, S. Dodd, F. N. Jacka and e. al, "Fusing data mining, machine learning and tradi tional statistics to detect biomarkers associated with depression," PLoS One, vol. 11(2), pp. 1-23, 2016.
[14] M. Pirooznia, F. Seifuddin, J. Judy, P. Mahon, J. Potash and P. Zandi, "Data mining approaches for genome-wide asscociation of mood disorders," Psychiatr: Genet, vol. 22(2), pp. 55-61, 2012.
[15] H. Ni, X. Yang, C. Fang, Y. Guo, M. Xu and Y. He, "Data mining-based study on sub-mentally healthy state among residents in eight provinces and cities in China," J Tradit. Chinese Med, vol. 34(4), pp. 511-517, 2014.
[16] Sepideh Jenabi, Ghasem Kahe, “A Review of Depression Detection by Data Mining Methods”, 8th National Conference on Computer Science and Engineering and Information Technology, Iran. https://www.civilica.com/Paper-CECCONF08-CECCONF08_031.html
[17] Chang YS, Hung WC, Juang TY. "Depression diagnosis based on ontologies and bayesian networks". Proc - 2013 IEEE Int. Conf. Syst. Man., Cybern. SMC. ;3452-3457, 2013.
[18] Thanathamathee, P., "Boosting with feature selection technique for screening and predicting adolescents’ depression". 2014 4th Int Conf Digit Inf Commun Technol Its Appl DICTAP. ;23-27, 2014.
[19]Ghafoor, Y., Huang, Y. P., and Liu, S. I., "An intelligent approach to discovering common symptoms among depressed patients". Soft. Comput. 19(4):819-827, 2015.
[20]Hou, Y., Xu, J., Huang, Y, and Ma, X., "A big data application to predict depression in the university based on the reading habits". 2016 3rd Int. Conf Syst. Inform.,! CSA! ;1085-1089, 2016.
[21] Husain, W., Xin, L. K., Rashid, N. A., and Jothi, N., "Predicting generalized anxiety disorder among women using random forest approach". 2016 3rd Int ConfComput Inf Sci. ;37-42, 2016.
[22] Li, X., Hu, B., Sun, S., and Cai, H., "EEG-based mild depressive detection using feature selection methods and classifiers". Comput. Methods Programs Biomed. 136:151-161, 2016.
[23] Nie, Z., Gong, P., and Ye, J., "Predict risk ofrelapse for patients with multiple stages of treatment of depression". Proc. 22Nd ACM SIGKDD Int. Conf Knowl. Discov. Data Min.; 1795-1804, 2016.
[24] Spyrou, I. M., Frantzidis, C., Bratsas, C., Antoniou, I., and Bamidis, P. D., "Geriatric depression symptoms coexisting with cognitive decline: A comparison of classification methodologies". Bio med. Sign. Process Contrl. 25:118-129, 2016.
[25] Kim, J. Y., Liu, N., Tan, H. X., and Chu, C.H., "Unobtrusive mon itoring to detect depression for elderly with chronic illnesses". IEEE Sens. J 17(17):5694- 5704, 2017.
[26] Hadzic, M., Hadzic, F., and Dillon, T. S., Mining of patient data: "Towards better treatment strategies for depression". Int. J Funct. Inform. Personal. Med. 3(2):122-143, 2010.
[27] Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.
[28] Ranno Agarwal “Genetic Algorithm in Data Mining”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 9, September 2015
[29] Ghassan A.; Clay G.; Naser E.; Su Y.; Rajasree H. N.; Israa A. “Intelligent data mining and machine learning for mental health diagnosis using genetic algorithm” 2015 IEEE International Conference on Electro/Information Technology (EIT). DOI: 10.1109/EIT.2015.7293425
[30] Subhagata C. “A neuro-fuzzy approach for the diagnosis of depression” https://doi.org/10.1016/j.aci.2014.01.001
[31] K.S.Chandrasekaran, V.Mahalakshmi , M R Anantha Padmanaban ,”Measuring Performance Reliability of a system using Data Mining” , 2019 11th International Conference on Advanced Computing (ICoAC) 978-1-7281-5286-8/IEEE 10.1109/ICoAC48765.2019.246834, ©2020
[32] Mansah, “A Tour of Evaluation Metrics for Machine Learning”, https://www.analyticsvidhya.com/blog /2020 / 11/a-tour-of-evaluation-metrics-for-machine-learning/#, November 24, 2020.
[33] M. Finkelstein, “Failure Rate Modeling for Reliability and Risk” Springer Science & Business Media, 2008 M11 7
[34] Sepideh Jenabi, Ghasem Kahe, “A Review of Depression Detection by Data Mining Methods”, 8th National Conference on Computer Science and Engineering and Information Technology, Iran. https://www.civilica.com/Paper-CECCONF08-CECCONF08_031.html