Skip to main content

Advertisement

SpringerLink
Log in

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

A psychological disorder is a mutilation state of the body that intervenes the imperative functioning of the mind or brain. In the last few years, the number of psychological disorders patients has been significantly raised. This paper presents a comprehensive review of some of the major human psychological disorders (stress, depression, autism, anxiety, Attention-deficit hyperactivity disorder (ADHD), Alzheimer, Parkinson, insomnia, schizophrenia and mood disorder) mined using different supervised and nature-inspired computing techniques. A systematic review methodology based on three-dimensional search space i.e. disease diagnosis, psychological disorders and classification techniques has been employed. This study reviews the discipline, models, and methodologies used to diagnose different psychological disorders. Initially, different types of human psychological disorders along with their biological and behavioural symptoms have been presented. The racial effects on these human disorders have been briefly explored. The morbidity rate of psychological disordered Indian patients has also been depicted. The significance of using different supervised learning and nature-inspired computing techniques in the diagnosis of different psychological disorders has been extensively examined and the publication trend of the related articles has also been comprehensively accessed. The brief details of the datasets used in mining these human disorders have also been shown. In addition, the effect of using feature selection on the predictive rate of accuracy of these human disorders is also presented in this study. Finally, the research gaps have been identified that witnessed that there is a full scope for diagnosis of mania, insomnia, mood disorder using emerging nature-inspired computing techniques. Moreover, there is a need to explore the use of a binary or chaotic variant of different nature-inspired computing techniques in the diagnosis of different human psychological disorders. This study will serve as a roadmap to guide the researchers who want to pursue their research work in the mining of different psychological disorders.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Antony, M. M., and Barlow, D. H., Handbook of assessment and treatment planning for psychological disorders. New York: The Guilford Press, 2011.

    Google Scholar 

  2. Steel, Z., Marnane, I. C., Chey, T., Jackson, J. W., Patel, V., and Silove, D., The Global prevalence of Common Mental Disorders: A Systematic Review and Meta-Analysis 1980-2013. Int. J. Epidemiol. 43(2):476–493, 2014.

    PubMed  PubMed Central  Google Scholar 

  3. Charles, S., and Walinga, J., Defining Psychological Disorders. In: Introduction to Psychology (2014) 1st Canadian Edition, 2014.

  4. Brewin, C. R., Gregory, J. D., Lipton, M., and Burgess, N., Intrusive images in psychological disorders: Characteristics, neural mechanisms, and treatment implications. Psychol. Rev. 117(1):210–232, 2010.

    PubMed  PubMed Central  Google Scholar 

  5. Walker, E. R., McGee, R. E., and Druss, B. G., Mortality in Mental Disorders and Global Disease Burden Implications: A Systematic Review and Meta-analysis. JAMA Psychiatry 72(4):334–341, 2015.

    PubMed  PubMed Central  Google Scholar 

  6. Wittchen, H. U., Mühlig, S., and Beesdo, K., Mental Disorders in Primary Care. Dialogues Clin. Neurosci. 5(2):115–128, 2003.

    PubMed  PubMed Central  Google Scholar 

  7. Serrano-Blanco, A., Palao, D. J., Luciano, J. V., Pinto-Meza, A., Luján, L., Fernández, A., Roura, P., Bertsch, J., Mercader, M., and Haro, J. M., Prevalence of mental disorders in primary care: results from the diagnosis and treatment of mental disorders in primary care study (DASMAP). Soc Psychiat Epidemiol. 45:201, 2010.

    Google Scholar 

  8. Matschinger, H., and Angermeyer, M. C., Lay beliefs about the causes of mental disorders: a new methodological approach. Soc. Psychiatry Psychiatr. Epidemiol. 31:309–315, 1996.

    CAS  PubMed  Google Scholar 

  9. Ahn, W. K., Proctor, C. C., and Flanagan, E. H., Mental Health Clinicians’ Beliefs About the Biological, Psychological, and Environmental Bases of Mental Disorders. Cogn. Sci. 33(2):147–182, 2009.

    PubMed  PubMed Central  Google Scholar 

  10. Mechanic, D., and McAlpine, D. D., The Influence of Social Factors on Mental Health. Principles and Practice of Geriatric Psychiatry. Hoboken: Wiley, 2002.

    Google Scholar 

  11. Merikangas, K. R., Jin, R., He, J., Kessler, R. C., Lee, S., Sampson, N. A., Viana, M. C., Andrade, L. H., Hu, C., Karam, E. G., Ladea, M., Medina-Mora, M. E., Ono, Y., Posada-Villa, J., Sagar, R., Wells, J. E., and Zarkov, Z., Prevalence and Correlates of Bipolar Spectrum Disorder in the World Mental Health Survey Initiative. Arch. Gen. Psychiatry 68(3):241–251, 2011.

    PubMed  PubMed Central  Google Scholar 

  12. Ströhle, A., Physical activity, exercise, depression and anxiety disorders. J. Neural Transm. 116:777, 2009.

    PubMed  Google Scholar 

  13. Morin, C. M., and Benca, R., Chronic Insomnia. Lancet 379(9821):1129–1141, 2012.

    PubMed  Google Scholar 

  14. Billiard, M., Jaussent, I., Dauvilliers, Y., and Besset, A., Recurrent Hypersomnia: A review of 339 cases. Sleep Med. Rev. 15(4):247–257, 2011.

    PubMed  Google Scholar 

  15. Zandi, M. S., Irani, S. R., Lang, B., Waters, P., Jones, P. B., McKenna, P., Coles, A. J., Vincent, A., and Lennox, B. R., Disease-relevant autoantibodies in first episode schizophrenia. J. Neurol. 258(4):686–688, 2011.

    PubMed  Google Scholar 

  16. Gilbert, J. A., Brown, R. K., Porazinska, D. L., Weiss, S. J., and Knight, R., Toward Effective Probiotics for Autism and Other Neurodevelopmental Disorders. Cell 155(7):1446–1448, 2013.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Mythili, M. S., and Shanavas, A. R. M., A Study on Autism Spectrum Disorders using Classification Techniques. International Journal of Soft Computing and Engineering 4(5):88–91, 2014.

    Google Scholar 

  18. Jiawei, H., Micheline, K., and Jian, P., Data Mining: Concepts and Techniques. 3rd edition. Amsterdam: Elsevier, 2013.

    Google Scholar 

  19. Sharma, M., Sharma, S., and Singh, G., Performance Analysis of Statistical and Supervised Learning Techniques in Stock Data Mining. Data MDPI 3(54):1–16, 2018.

    Google Scholar 

  20. Sharma, M., Singh, G., and Singh, R., An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders. EAI Endorsed Transactions on Scalable Information Systems 5(18):1–11, 2018.

    Google Scholar 

  21. Sharma, M., Singh, G., and Singh, R., Stark Assessment of Lifestyle Based Human Disorders Using Data Mining Based Learning Techniques. IRBM 38:305–324, 2017.

    Google Scholar 

  22. Kaur, P., and Sharma, M., A Survey on Using Nature Inspired Computing for Fatal Disease Diagnosis. International Journal of Information System Modeling and Design 8(2):70–91, 2017.

    Google Scholar 

  23. Arora, S., Singh, H., Sharma, M., and Sharma, S., Anand P (2019) A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection. IEEE Access 7:26343–26361, 2019.

    Google Scholar 

  24. Sharma, M., Singh, G., and Singh, R., Clinical decision support system query optimizer using hybrid Firefly and controlled Genetic Algorithm. Journal of King Saud University-Computer and Information Sciences, 2018 In press.

  25. Sharma, M., Singh, G., and Singh, R., A review of different cost-based distributed query optimizers. Progress in Artificial Intelligence 8(1):45–62, 2018.

    Google Scholar 

  26. Holland, J., Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press, 1992.

    Google Scholar 

  27. Eberhart, R., Kennedy, J., Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks 4, 1995.

  28. Dorigo, M., and Gambardella, L. M., Ant colony system: a cooperative learning approach to the travelling salesman problem. IEEE Trans. Evol. Comput. 1(1):53–66, 1997.

    Google Scholar 

  29. Geem, Z. W., Kim, J. H., and Loganathan, G. V., A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68, 2001.

    Google Scholar 

  30. Karaboga, D., and Ozturk, C., A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl. Soft Comput. 11(1):652–657, 2011.

    Google Scholar 

  31. Yang, X-S, and Deb, S., Cuckoo search via Lévy flights. Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on. IEEE, 2009.

  32. Yang, X.-S., Flower Pollination Algorithm for Global Optimization, International Conference on Unconventional Computing and Natural Computation, UCNC 2012: Unconventional Computation and Natural Computation 7445: 240–249, 2012.

  33. Mirjalili, S., The ant lion optimizer. Adv. Eng. Softw. 83:80–98, 2015.

    Google Scholar 

  34. Mirjalili, S., Mirjalili, S. M., and Lewis, A., Grey wolf optimizer. Adv. Eng. Softw. 69:46–61, 2014.

    Google Scholar 

  35. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. https://doi.org/10.1176/appi.books.9780890425596. Accessed 3rd June 2018.

  36. Mental Health Facts Multicultural. National Alliance on Mental Illness. Link: https://www.nami.org/NAMI/media/NAMI-Media/Infographics/MulticulturalMHFacts10-23-15.pdf. Accessed 24th May 2018.

  37. Murthy, R. S., National Mental Health Survey of India 2015–2016. Indian J. Psychiatry 59(1):21–26, 2017.

    PubMed  PubMed Central  Google Scholar 

  38. Piatetsky-Shapiro, G., An Overview of Knowledge Discovery in Databases: Recent Progress and Challenges. In: Ziarko, W. P. (Ed.), Rough Sets, Fuzzy Sets and Knowledge Discovery. London: Workshops in Computing. Springer, 1994.

    Google Scholar 

  39. Ramzan, M., and Ahmad, M., Evolution of Data Mining: An overview. In: Conference on IT in Business, Industry and Government (CSIBIG), Indore 1–4, 2014.

  40. Gorunescu, F., Data Mining Concepts, Models and Techniques. Springer, 2011.

  41. Sumathi, S., and Sivanandam, S. N., Data Mining Tasks, Techniques, and Applications. In: Introduction to Data Mining and its Applications, New York: Springer-Verlag Berlin Heidelberg: 195–216, 2006.

  42. Ian, W., and Eibe, F., Data Mining: Practical Machine Learning Tools and Techniques. 2nd edition. Amsterdam: Elsevier, 2005.

    Google Scholar 

  43. Nikam, S. S., A Comparative Study of Classification Techniques in Data Mining Algorithms. Oriental Journal of Computer Science and Technology 8(1):13–19, 2015.

    Google Scholar 

  44. Saeys, Y., Inza, I., and Larrañaga, P., A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517, 2007.

    CAS  PubMed  Google Scholar 

  45. Tang, J., and Liu, H., Feature selection with linked data in social media. In: Proceedings of the 2012 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2012.

  46. Feizollah, A., Anuar, N. B., Salleh, R., and Wahab, A. W. A., A review on feature selection in mobile malware detection. Digit. Investig. 13:22–37, 2015.

    Google Scholar 

  47. Krishnanand, K. N., and Ghose, D., Glow-worm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intelligence 3(2):87–124, 2009.

    Google Scholar 

  48. Hosseini, H. S., Problem-solving by intelligent water drops. 2007 IEEE congress on evolutionary computation. IEEE, 2007.

  49. Mucherino, A., and Seref, O., Monkey search: a novel meta-heuristic search for global optimization. AIP Conference Proceedings. 953(1):162–173, 2007.

    CAS  Google Scholar 

  50. Simon, D., Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6):702–713, 2008.

    Google Scholar 

  51. Yang, X. S., Firefly algorithm Nature-Inspired Metaheuristic Algorithms. Cambridge: Luniver Press, 2008, 79–90.

    Google Scholar 

  52. Yang, X. S., A New Metaheuristic Bat-Inspired Algorithm. In: González, J. R., Pelta, D. A., Cruz, C., Terrazas, G., Krasnogor, N. (Eds), Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, 284. Berlin, Heidelberg: Springer, 2010.

    Google Scholar 

  53. Mirjalali, S., Moth-Flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89:228–249, 2015.

    Google Scholar 

  54. Mirjalili, S., Dragonfly Algorithm: A New Meta-Heuristic Optimization Technique for Solving Single-objective, Discrete, and Multi-objective Problems. Neural Comput. & Applic. 27(4):1053–1073, 2015.

    Google Scholar 

  55. Mirjalili, S., Mirjalili, S. M., and Hatamlou, A., Multi-Verse Optimizer: a nature-inspired algorithm for global optimization. Neural Comput. & Applic. 27(2):495–513, 2016.

    Google Scholar 

  56. Chen, C.-C., Tsai, Y.-C., Liu, I.-I. et al., A Novel Metaheuristic: Jaguar Algorithm with Learning Behavior. Kowloon: IEEE International Conference on Systems, Man, and Cybernetics, 2015.

    Google Scholar 

  57. Yazdani, M., and Jolai, F., Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm. J Comput Des Eng. 3(1):24–36, 2016.

    Google Scholar 

  58. Hosseini, E., Laying Chicken Algorithm: A New Meta-Heuristic Approach to Solve Continuous Programming Problems. J Appl Computat Math 6:344, 2017.

    Google Scholar 

  59. Jangir, P., Parmar, S., and Trivedi, I. N., Human Behavior Based Optimization Algorithm For Optimal Power Flow Problem With Discrete And Continuous Control Variables. International Journal of Engineering Technology Research & Management 1(1):26–35, 2017.

    Google Scholar 

  60. Zolghadr-Asli, B., Bozorg-Haddad, O., and Chu, X., Crow Search Algorithm (CSA). In: Bozorg-Haddad, O. (Ed.), Advanced Optimization by Nature-Inspired Algorithms. Studies in Computational Intelligence. Vol. 720. Singapore: Springer, 2018, 143–149.

    Google Scholar 

  61. Zhang, J., Xiao, M., Gao, L., and Pan, Q., Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Appl. Math. Model. 63:464–490, 2018.

    Google Scholar 

  62. Sasan, H., Khalilian, M., Mohammadzadeh, J., and Ebrahimnejad, S., Emperor Penguins Colony: a new metaheuristic algorithm for optimization. Evol. Intell. 2019:1–16, 2019.

    Google Scholar 

  63. Deziel, M., Olawo, D., Truchon, L., and Golab, L., Analyzing the mental health of engineering students using classification and regression. EDM 2013:228–231, 2013.

    Google Scholar 

  64. Kiruthika, K., Veerajayasri, V., Lavanya, M., and Surya, M., Analyzing Stress on Social Media through Data mining. International Journal of Innovative Research in Computer and Communication Engineering 4(11):19270–19274, 2016.

    Google Scholar 

  65. Umanandhini, D., and Kalpana, G., Survey on Stress Types using Data Mining Algorithms. International Journal of Innovative Research in Advanced Engineering 4(4):47–51, 2017.

    Google Scholar 

  66. Marinić, I., Supek, F., Kovačić, Z., Rukavina, L., Jendričko, T., and Kovačić, D. K., Posttraumatic Stress Disorder: Diagnostic Data Analysis by Data Mining Methodology. Croat Med J. 48:185–197, 2007.

    PubMed  PubMed Central  Google Scholar 

  67. Yoon, S., Taha, B., and Bakken, S., Using a Data Mining Approach to Discover Behavior Correlates of Chronic Disease: A Case Study of Depression. Stud Health Technol Inform. 201:71–78, 2014.

    PubMed  PubMed Central  Google Scholar 

  68. Mohammadi, M., Al-Azab, F., Raahemi, B., Richards, G., Jaworska, N., Smith, D. et al., Data mining EEG signals in depression for their diagnostic value. BMC Med Inform Decis Mak 15(108):1–14, 2015.

    Google Scholar 

  69. Mwangi, B., Ebmeier, K. P., Matthews, K., and Steele, J. D., Multi-centre diagnostic classification of individual structural Neuroimaging scans from patients with major depressive disorder. Brain A Journal of Neurology 135:1508–1521, 2012.

    PubMed  Google Scholar 

  70. Daimi, K., and Banitaan, S., Using Data Mining to Predict Possible Future Depression Cases. Int J Publ Health Sci 3(4):231–240, 2014.

    Google Scholar 

  71. Dipnall, J. F., Pasco, J. A., Berk, M., Williams, L. J., Dodd, S., Jacka, F. N. et al., Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression. PLoS One 11(2):1–23, 2016.

    Google Scholar 

  72. Maroco, J., Silva, D., Rodrigues, A., Guerreiro, M., Santana, I., and Mendonça, A., Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res Notes 4(299):1–14, 2011.

    Google Scholar 

  73. Benyoussef, E. M., Elbyed, A., and El Hadiri, H., Data Mining Approaches for Alzheimer’s Disease Diagnosis. In: Sabir, E., García Armada, A., Ghogho, M., Debbah, M. (Eds), Ubiquitous Networking. Berlin: Springer, 2017, 619–631.

    Google Scholar 

  74. Doyle, O. M., Westman, E., Marquand, A. F., Mecocci, P., Vellas, B. et al., Predicting Progression of Alzheimer’s Disease Using Ordinal Regression. PLoS One 9(8):1–10, 2014.

    Google Scholar 

  75. Johnson, P., Vandewater, L., Wilson, W., Maruff, P., Savage, G., Graham, P. et al., Genetic algorithm with logistic regression for prediction of progression to Alzheimer’s disease. BMC Bioinformatics 15:1–14, 2014.

    Google Scholar 

  76. Koikkalainen, J., Pӧlӧnen, H., Mattila, J., van Gils, M., Soininen, H. et al., Improved Classification of Alzheimer’s Disease Data via Removal of Nuisance Variability. PLoS One 7(2), 2012.

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Lama, R. K., Gwak, J., Park, J. S., and Lee, S. W., Diagnosis of Alzheimer’s Disease based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features. J Healthc Eng 2017:1–11, 2017.

    Google Scholar 

  78. Hasan, C. Z. C., Jailani, R., Tahir, N., Yassin, I. M., and Rizman, Z. I., Automated Classification of Autism Spectrum Disorders Gait Patterns Using Discriminant Analysis Based on Kinematic and Kinetic Gait Features. Journal of Applied Environmental and Biological Sciences 7(1):150–156, 2017.

    Google Scholar 

  79. Grossi, E., Olivieri, C., and Buscema, M., Diagnosis of autism through EEG processed by advanced computational algorithms: a pilot study. Comput. Methods Prog. Biomed. 142:73–79, 2017.

    Google Scholar 

  80. Kundra, D., and Pandey, B., Classification of EEG based Diseases using Data Mining. Int. J. Comput. Appl. 90(18):11–15, 2014.

    Google Scholar 

  81. Huang, Q. R., Qin, Z., Zhang, S., and Chow, C. M., Clinical Patterns of Obstructive Sleep Apnea and Its Comorbid Conditions: A Data Mining Approach. J. Clin. Sleep Med. 4(6):543–550, 2008.

    PubMed  PubMed Central  Google Scholar 

  82. Khemphila, A., and Boonjing, V., Parkinsons Disease Classification using Neural Network and Feature selection. Int. J. Math. Comput. Sci. 6(4):377–380, 2012.

    Google Scholar 

  83. Mohana, E., and Poonkuzhali, S., Categorizing the Risk Level of Autistic Children using Data Mining techniques. International Journal of Advance Research in Science and Engineering 4(1):223–230, 2015.

    Google Scholar 

  84. McManus, K., Mallory, E. K., Goldfeder, R. L., and Winston, A., Mining Twitter Data to Improve Detection of Schizophrenia. AMIA Jt Summits Transl Sci Proc. 25:122–126, 2015.

    Google Scholar 

  85. Kim, J. W., Sharma, V., and Ryan, N. D., Predicting Methylphenidate Response in ADHD Using Machine Learning Approaches. Int. J. Neuropsychopharmacol. 2015:1–7, 2015.

    Google Scholar 

  86. Radhamani, E., and Krishnaveni, K., Diagnosis and Evaluation of ADHD using MLP and SVM Classifiers. Indian J. Sci. Technol. 9(19):1–7, 2016.

    Google Scholar 

  87. Kim, M. H., Banerjee, S., Park, S. M., and Pathak, J., Improving risk prediction for depression via Elastic Net regression - Results from Korea National Health Insurance Services Data. AMIA Annu Symp Proc. 10(2016):1860–1869, 2017.

    Google Scholar 

  88. Ramani, R. G., and Sivaselvi, K., Autism Spectrum Disorder Identification Using Data Mining Techniques. Int J Pure Appl Math 117(16):427–436, 2017.

    Google Scholar 

  89. Bekerom, B., Using Machine Learning for Detection of Autism Spectrum Disorder. Enschede: 26th Twente Student Conference on IT Feb 3th, 2017, 1–7.

    Google Scholar 

  90. Tejeswinee, K., Jacobb, S. G., and Athilakshmi, R., Feature Selection Techniques for Prediction of Neuro-Degenerative Disorders: A Case-Study with Alzheimer’s And Parkinson’s Disease. Procedia Comput Sci 115:188–194, 2017.

    Google Scholar 

  91. Aram, S., Hooshyar, D., Park, K. W., and Lim, H. S., Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques. Appl. Sci. 7(651):1–17, 2017.

    Google Scholar 

  92. Bae, Y., Kumarasamy, K., Ali, I. M., Korfiatis, P., Akkus, Z., and Erickson, B. J., Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI. J. Digit. Imaging 31(2):252–261, 2018.

    PubMed  Google Scholar 

  93. Algunaid, R. F., Algumaei, A. H., Rushdi, M. A., and Yassine, I. A., Schizophrenic patient identification using graph-theoretic features of resting-state fMRI data. Biomed Signal Process Control 43:289–299, 2018.

    Google Scholar 

  94. Hosseinifard, B., Moradi, M. H., and Rostami, R., Classifying depression patients and normal subjects using machine learning techniques and non-linear features from EEG signal. Computer Methods Programs 109(3):339–345, 2013.

    Google Scholar 

  95. Xiao, H., Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine with Acoustic Characteristics. In: 5th International Conference on Biomedical Engineering and Informatics (BMEI 2012), IEEE; 1072–1076, 2012.

  96. Hiesh, M.-H., Andy, Y.-.Y. L., Shen, C.-P., Chen, W., Lin, F.-S., Sung, H.-Y., Lin, J.-W., Chiu, M.-J., and Lai, F., Classification of Schizophrenia using Genetic Algorithm-Support Vector Machine (GA-SVM). In: 35th Annual International Conference of the IEEE EMBS Osaka, IEEE, 6047–6050, 2013.

  97. Sivapriya, R. T., Nadira Banu Kamal, A. R., and Thavavel, V., Automated Classification of Dementia Using PSO based Least Square Support Vector Machine. Int J Mach Learn Comput. 3(2):181–185, 2013.

    Google Scholar 

  98. Hang, L.-W., Lin, H.-H., Chiang, Y.-W. et al., Diagnosis of Severe Obstructive Sleep Apnea with Model Designed Using Genetic Algorithm and Ensemble Support Vector Machine. Appl. Math. Inf. Sci. 7(1):227S–336S, 2013.

    Google Scholar 

  99. Yang, S.-T., Lee, J.-D., Chang, T.-C., Huang, C.-H., Wang, J.-J., Hsu, W.-C., Chan, H.-L., Wai, Y.-Y., and Li, K.-Y., Discrimination between Alzheimer’s Disease and Mild Cognitive Impairment Using SOM and PSO-SVM. Comput Math Methods Med 2013:1–10, 2013.

    Google Scholar 

  100. Shahbakhi, M., Far, D. T., and Tahami, E., Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine. J. Biomed. Sci. Eng. 7:147–156, 2014.

    Google Scholar 

  101. Abedi, Z., Naghavi, N., and Rezaeitalab, F., Detection and classification of sleep apnea using genetic algorithms and SVM-based classification of thoracic respiratory effort and oximetric signal features. Comput. Intell. 2017:1–14, 2017.

    Google Scholar 

  102. Mohammadi, M., Al-Azab, F., Raahemi, B., Richards, G., Jaworska, N., and Smith, D., Data mining EEG signals in depression for their diagnostic value. BMC Med Inform Decis Mak 15:108, 2015.

    PubMed  PubMed Central  Google Scholar 

  103. Naskar, S., Detection of Parkinson’s disease using Neural Network Trained with Genetic Algorithm. Int. J. Adv. Res. Comput. Sci. 7(5):46–51, 2016.

    Google Scholar 

  104. Ranjith, C., and Mohanapriya, 2. M., A Feed-Forward Neural Network with Particle Swarm Optimization based Classification Scheme for Stress Detection from EEG Signals and Reduction of Stress Using Music. Int J Pure Appl Math 117(20):643–659, 2017.

    Google Scholar 

  105. Sayed, G. I., Hassanien, A. E., Nassef, T. M., and Pan, J.-S., Alzheimer’s Disease Diagnosis Based on Moth Flame Optimization. Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing 536:298–305, 2017.

    Google Scholar 

  106. Vaishali, R., and Sasikala, R., A machine learning based approach to classify Autism with optimum behaviour sets. International Journal of Engineering & Technology 7(4):18, 2018.

    Google Scholar 

  107. Shon, D., Im, K., Park, J.-H. et al., Emotional Stress State Detection Using Genetic Algorithm-Based Feature Selection on EEG Signals. Int. J. Environ. Res. Public Health 15:2461–2471, 2018.

    PubMed Central  Google Scholar 

  108. Li, W., Zhou, T., Zou, L., Lu, J., Liu, H., and Wang, S., Identification of Attention Deficit/Hyperactivity Disorder in Children Using Multiple ERP Features. Curr. Bioinforma. 13(5):501–507, 2018.

    CAS  Google Scholar 

  109. Wolfe, F., and Michaud, K., Predicting Depression in Rheumatoid Arthritis: The Signal Importance of Pain Extent and Fatigue, and Comorbidity. Arthritis Rheum. 61(5):667–673, 2009.

    PubMed  Google Scholar 

  110. Sumathi, M. R., and Poorna, B., Prediction of Mental Health Problems among Children Using Machine Learning Techniques. Int. J. Adv. Comput. Sci. Appl. 7(1):552–557, 2016.

    Google Scholar 

  111. Chandrashekar, G., and Sahin, F., A survey on feature selection methods. Comput. Electr. Eng. 40:16–28, 2014.

    Google Scholar 

  112. Rudnicki, W. R., Wrzesie’n M, Paja W (2015) All Relevant Feature Selection Methods and Applications. In: Stańczyk, U., Jain, L. C. (Eds), Feature Selection for Data and Pattern Recognition. New York: Springer-Verlag Berlin Heidelberg, 2015, 11–28.

    Google Scholar 

  113. Chaovalitwongse, W. A., Pottenger, R. S., Wang, S., Fan, Y. J., and Iasemidis, L. D., Pattern- and Network-Based Classification Techniques for Multichannel Medical Data Signals to Improve Brain Diagnosis. IEEE Trans. Syst. Man Cybern. Syst. Hum. 41(5):977–988, 2011.

    Google Scholar 

  114. Shree, S. R. B., Sheshadri, H. S., and Krishna, M., Diagnosis of Alzheimer’s Disease using Rule-based Approach. Indian J. Sci. Technol. 9(13):1–6, 2016.

    Google Scholar 

  115. Usman, S. M., Usman, M., and Fong, S., Epileptic Seizures Prediction Using Machine Learning Methods. Comput Math Methods Med 2017:1–10, 2017.

    Google Scholar 

  116. Lin, C. T., Prasad, M., Chung, C. H., Puthal, D., Sayed, H. E., Sankar, S. et al., IoT-Based Wireless Polysomnography Intelligent System for Sleep Monitoring. Special Section on Intelligent Systems for the Internet of Things 6:405–414, 2018.

    Google Scholar 

  117. Mora, H., Gil, D., Terol, R. M., Azorín, J., and Szymanski, J., An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments. Sensors 17(2302):1–25, 2017.

    Google Scholar 

  118. Lalo, E., Riff, J., Parry, R., Jabloun, M., Roussel, J., Chen, C. et al., Design of Technology and Technology of Design. Activity Analysis as a Resource for a Personalised Approach for Patients with Parkinson Disease. IRBM 37(2):90–97, 2016.

    Google Scholar 

  119. Hariharan, M., Polat, K., and Sindhu, R., A new hybrid intelligent system for accurate detection of Parkinson's disease. Comput. Methods Prog. Biomed. 113(3):904–913, 2014.

    CAS  Google Scholar 

  120. Gupta, D., Sundarama, S., Khanna, A. et al., Improved diagnosis of Parkinson’s disease based on Optimized crow search Algorithm. Comput. Electr. Eng. 68:412–424, 2018.

    Google Scholar 

  121. Sharma, P., Sundaram, S., Sharma, M. et al., Diagnosis of Parkinson’s disease using modified grey wolf optimization. Cogn. Syst. Res. 54:100–115, 2018.

    Google Scholar 

  122. Gupta, D., Julka, A., Jain, S. et al., Optimized cuttlefish algorithm for diagnosis of Parkinson’s disease. Cogn. Syst. Res. 52:36–48, 2018.

    Google Scholar 

  123. Al-Fatlawi A. H., Jabardi M. H., and Ling S. H., An efficient diagnosis system for Parkinson's disease using deep belief network. Congress on Evolutionary Computation (CEC) IEEE, 2016.

  124. Lin, F., Zhuang, Y., Song, C., Wang, A., Li, Y., Gu, C., Li, C., and Xu, W., A Noncontact and Cost-Effective Sleep Monitoring System. IEEE Transactions on Biomedical Circuits and Systems 11(1):189–202, 2017.

    PubMed  Google Scholar 

  125. Sunsirikul, S., and Achalakul, T., Associative Classification Mining in the Behavior Study of Autism Spectrum Disorder. IEEE 3:279–283, 2010.

    Google Scholar 

  126. Abibullaev, B., Decision Support Algorithm for Diagnosis of ADHD Using Electroencephalograms. J. Med. Syst. 36:2675–2688, 2012.

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSA, DAV University, Jalandhar, India

    Prableen Kaur & Manik Sharma

Authors
  1. Prableen Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manik Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manik Sharma.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Systems-Level Quality Improvement

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, P., Sharma, M. Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis. J Med Syst 43, 204 (2019). https://doi.org/10.1007/s10916-019-1341-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-019-1341-2

Keywords

Search

Navigation