Skip to main content

Advertisement

SpringerLink
Log in

Data Mining Algorithms and Techniques in Mental Health: A Systematic Review

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

Abstract

Data Mining in medicine is an emerging field of great importance to provide a prognosis and deeper understanding of disease classification, specifically in Mental Health areas. The main objective of this paper is to present a review of the existing research works in the literature, referring to the techniques and algorithms of Data Mining in Mental Health, specifically in the most prevalent diseases such as: Dementia, Alzheimer, Schizophrenia and Depression. Academic databases that were used to perform the searches are Google Scholar, IEEE Xplore, PubMed, Science Direct, Scopus and Web of Science, taking into account as date of publication the last 10 years, from 2008 to the present. Several search criteria were established such as ‘techniques’ AND ‘Data Mining’ AND ‘Mental Health’, ‘algorithms’ AND ‘Data Mining’ AND ‘dementia’ AND ‘schizophrenia’ AND ‘depression’, etc. selecting the papers of greatest interest. A total of 211 articles were found related to techniques and algorithms of Data Mining applied to the main Mental Health diseases. 72 articles have been identified as relevant works of which 32% are Alzheimer’s, 22% dementia, 24% depression, 14% schizophrenia and 8% bipolar disorders. Many of the papers show the prediction of risk factors in these diseases. From the review of the research articles analyzed, it can be said that use of Data Mining techniques applied to diseases such as dementia, schizophrenia, depression, etc. can be of great help to the clinical decision, diagnosis prediction and improve the patient’s quality of life.

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

Similar content being viewed by others

References

  1. Dhaka, P., and Johari, R., Big data application: Study and archival of mental health data, using MongoDB. Int. Conf. Electr. Electron. Optim. Tech.:3228–3232, 2016.

  2. 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.

    Article  CAS  Google Scholar 

  3. Pirooznia, M., Seifuddin, F., Judy, J., Mahon, P., Potash, J., and Zandi, P., Data mining approaches for genome-wide asscociation of mood disorders. Psychiatr. Genet. 22(2):55–61, 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Ni, H., Yang, X., Fang, C., Guo, Y., Xu, M., and He, Y., Data mining-based study on sub-mentally healthy state among residents in eight provinces and cities in China. J. Tradit. Chinese Med. 34(4):511–517, 2014.

    Article  Google Scholar 

  5. (WHO) WHO. Trastornos mentales. 2018; Available from: http://www.who.int/mediacentre/factsheets/fs396/es/ (last accessed April 2018).

  6. Mathew, J., Mekkayil, L., Ramasangu, H., Karthikeyan, B. R., and Manjunath, A. G., Robust algorithm for early detection of Alzheimer’s disease using multiple feature extractions. IEEE Annu. India Conf. 2016:1–6, 2016.

  7. Bhagya Shree, S. R., and Sheshadri, H. S., An initial investigation in the diagnosis of Alzheimer’s disease using various classification techniques. 2014 IEEE Int Conf Comput Intell Comput Res. 2014;1–5.

  8. Qu, X., Yuan, B., and Liu, W., A predictive model for identifying possible MCI to AD Conversions in the ADNI database. 2009 2nd Int. Symp. Knowl. Acquis. Model KAM 3:102–105, 2009.

  9. Gironi M, Borgiani B, Farina E, Mariani E, Cursano C, Alberoni M, et al. A global immune deficit in Alzheimer’s disease and mild cognitive impairment disclosed by a novel data mining process. J. Alzheimers Dis. 2015;43(4):1199–1213

  10. 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 

  11. Lee, C., Lam, C. P., and Masek, M., Rough-fuzzy hybrid approach for identification of bio-markers and classification on Alzheimer’s disease data. Proc. 2011 11th IEEE Int Conf Bioinforma Bioeng BIBE. ;84–91, 2011.

  12. Alonso, S. G., de la Torre Díez, I., Rodrigues, J. J. P. C., Hamrioui, S., and López-Coronado, M., A systematic review of techniques and sources of big data in the healthcare sector. J. Med. Syst. 41(11):183, 2017.

    Article  PubMed  Google Scholar 

  13. Khan, A., and Usman, M., Early diagnosis of Alzheimer’s disease using machine learning techniques. 2015 7th Int Jt Conf. Knowl. Discov. Knowl. Eng. Knowl. Manag. (IC3K) 1:380–387, 2015.

  14. Wongkoblap, A., Vadillo, M. A., and Curcin, V., Researching mental health disorders in the era of social media: Systematic review. J. Med. Internet Res. 19(6):e228, 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Yuan, C., Data mining techniques with its application to the dataset of mental health of college students. IEEE Work Adv. Res. Technol. Ind. Appl. WARTIA. 2014:391–393, 2014.

  16. Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J. F. et al., Data mining in healthcare and biomedicine: A survey of the literature. J. Med. Syst. 36(4):2431–2448, 2012.

    Article  PubMed  Google Scholar 

  17. Sarraf, S., and Tofighi, G., Deep learning-based pipeline to recognize Alzheimer’s disease using fMRI data. 2016 Future Technologies Conference (FTC), IEEE. 816–820, 2016.

  18. Fiscon, G., Weitschek, E., Felici, G., Bertolazzi, P., De Salvo, S., and Bramanti, P, et al., Alzheimer’s disease patients classification through EEG signals processing. 2014 IEEE Symp Comput Intell Data Min (CIDM).;105–112, 2014.

  19. Pachange S, Joglekar B, Kulkarni P. An ensemble classifier approach for disease diagnosis using random Forest. 2015 Annu. IEEE Ind. Conf.;1–5, 2015.

  20. Le, Q. B., Shafiq, O., and Alhajj, R., Analyzing Alzheimer’s disease gene expression dataset using clustering and association rule mining. Proc. 2014 IEEE 15th Int Conf Inf Reuse Integr IEEE IRI. ;283–290, 2014.

  21. Moon, S., Choi, B., An, J., and Yoon, T., Constructing a sorting machine for degenerative cerebropathia. Int. Conf. Adv. Commun. Technol. ICACT.;800–804, 2017.

  22. Hadzic M, Hadzic F, Dillon T. Tree mining in mental health domain. Proc. 41st Annu. Hawaii Int. Conf. Syst. Sci. ;1–8, 2008.

  23. Simon, G. J., Li, P. W., Jack, Jr. C. R., and Vemuri, P., Understanding atrophy trajectories in Alzheimer’s disease using association rules on MRI images. Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. ;369–376, 2011.

  24. Payandeh, S., Recursive Bayesian tracking for smart elderly living. 7th IEEE Annu Inf Technol Electron Mob Commun Conf IEEE IEMCON. ;1–7, 2016.

  25. Chiang, H.-S., and Pao, S.-C., An EEG-based fuzzy probability model for early diagnosis of Alzheimer’s disease. J. Med. Syst. 40(5):125, 2016.

    Article  PubMed  Google Scholar 

  26. Ertek, G., Tokdil, B., and Günaydın, İ., Risk factors and identifiers for Alzheimer’s disease: A data mining analysis. Ind. Conf. Data Min.;1–11, 2014.

  27. Joshi, S., Shenoy, D, G.G. VS, Rrashmi, P. L., Venugopal, K. R., and Patnaik, L. M., Classification of Alzheimer’s disease and Parkinson’s disease by using machine learning and neural network methods. 2010 Second Int. Conf. Mach. Learn. Comput. ;218–222, 2010.

  28. Plant, C., Teipel, S. J., Oswald, A., Böhm, C., Meindl, T., Mourao-Miranda, J. et al., Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. Neuroimage. 50(1):162–174, 2010.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Chaves, R., Górriz, J. M., Ramírez, J., Illn, I. A., Salas-Gonzalez, D., and Gómez-Río, M., Efficient mining of association rules for the early diagnosis of Alzheimer’s disease. Phys. Med. Biol. 56(18):6047–6063, 2011.

    Article  PubMed  CAS  Google Scholar 

  30. Plant C, Sorg C, Riedl V, Wohlschläger A. Homogeneity-based feature extraction for classification of early-stage alzheimer’s disease from functional magnetic resonance images. Proc. 2011 Work Data Min. Med. Healthc - DMMH. 2011;33–41, 2011.

  31. Al-Dlaeen, D., and Alashqur, A., Using decision tree classification to assist in the prediction of Alzheimer’s disease. 2014 6th Int. Conf. Comput. Sci. Inf. Technol. (CSIT).;122–126, 2014.

  32. Zhang, Y., Wang, S., and Dong, Z., Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Prog. Electromagn. Res. 144:171–184, 2014.

    Article  Google Scholar 

  33. Sheshadri, H. S. , Shree, S. R. B., and Krishna, M., Diagnosis of Alzheimer’s disease employing neuropsychological and classification techniques. Proc 2015 5th Int. Conf. IT Converg. Sec. ICITCS. ;1–6, 2015.

  34. Martínez-Ballesteros, M., García-Heredia, J. M., Nepomuceno-Chamorro, I. A., and Riquelme-Santos, J. C., Machine learning techniques to discover genes with potential prognosis role in Alzheimer’s disease using different biological sources. Inf. Fusion. 36:114–129, 2017.

    Article  Google Scholar 

  35. Tejeswinee, K., Shomona, G. J., and Athilakshmi, R., Feature selection techniques for prediction of neuro-degenerative disorders: A case-study with Alzheimer’s and Parkinson’s disease. Proc. Comput. Sci. 115:188–194, 2017.

    Article  Google Scholar 

  36. Jacob SG, Athilakshmi R. Extraction of protein sequence features for prediction of neuro-degenerative brain disorders: Pioneering the CGAP database. Proc Int Conf Informatics Anal - ICIA-16.;30, 2016.

  37. Aditya, C. R., and Pande, M. B. S., Devising an interpretable calibrated scale to quantitatively assess the dementia stage of subjects with alzheimer’s disease: A machine learning approach. Inform. Med. Unlocked. 6:28–35, 2017.

    Article  Google Scholar 

  38. Byeon, H. A., Prediction model for mild cognitive impairment using random forests. Int. J. Adv. Comput. Sci. Appl. 6(12):8–12, 2015.

    Google Scholar 

  39. Fernández-Llatas, C., García-Gomez, J. M., Vicente, J., Naranjo, J. C., Robles, M., and Benedí, J. M., et al., Behaviour patterns detection for persuasive design in nursing homes to help dementia patients. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS.;6413–6417, 2011.

  40. Wen, L., Bewley, M., Eberl, S., Fulham, M., and Feng, D. D., Classification of dementia from fdg-pet parametric images using data mining. 5th IEEE Int. Symp. Biomed. Imaging From Nano to Macro, ISBI. ;412–415, 2008.

  41. Zhang, S., Mcclean, S., Nugent, C., Neill, S. O., Donnelly, M., and Galway, L., et al., Prediction of assistive technology adoption for people with dementia. Int. Conf. Heal. Inf. Sci. ;160–171, 2013.

  42. Zhang S, McClean SI, Nugent CD, Donnelly MP, Galway L, Scotney BW, et al. A predictive model for assistive technology adoption for people with dementia. IEEE J. Biomed. Heal. Inform. 2014;18(1):375–383.

  43. Bang, S., Son, S., Roh, H., Lee, J., Bae, S., Lee, K. et al., Quad-phased data mining modeling for dementia diagnosis. BMC Med. Inform. Decis. Mak. 17(1):60, 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Joshi, S., Shenoy, P. D., Venugopal, K. R., and Patnaik, L. M., Evaluation of different stages of dementia employing neuropsychological and machine learning techniques. BT - 2009 1st Int. Conf. Adv. Comput. ICAC. ;154–160, 2009.

  45. Zanin, M., Sousa, P., Papo, D., Bajo, R., García-Prieto, J., Del Pozo, F. et al., Optimizing fun network representation of multivariate time series. Sci Rep. 2:630, 2012 1.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. 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.

  47. Jena, L., and Kamila, N. K., A model for prediction of human depression using Apriori algorithm. 2014 Int. Conf. Inf. Technol. ;240–244, 2014.

  48. Jung, Y., and Yoon, Y. I., Multi-level assessment model for wellness service based on human mental stress level. Multimed. Tools Appl. 76(9):11305–11317, 2017.

    Article  Google Scholar 

  49. Morales, S., Barros, J., Echávarri, O., García, 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.

    Google Scholar 

  50. 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(1):108, 2015.

    Article  PubMed  PubMed Central  Google Scholar 

  51. 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.

  52. 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.

  53. 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.

    Article  Google Scholar 

  54. 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., ICSAI. ;1085–1089, 2016.

  55. Husain, W., Xin, L. K., Rashid, N. A., and Jothi, N., Predicting generalized anxiety disorder among women using random forest approach. 2016 3rd Int Conf Comput Inf Sci. ;37–42, 2016.

  56. 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.

    Article  PubMed  Google Scholar 

  57. Nie, Z., Gong, P., and Ye, J., Predict risk of relapse for patients with multiple stages of treatment of depression. Proc. 22Nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min.;1795–1804, 2016.

  58. 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. Biomed. Sign. Process Contrl. 25:118–129, 2016.

    Article  Google Scholar 

  59. Kim, J. Y., Liu, N., Tan, H. X., and Chu, C. H., Unobtrusive monitoring to detect depression for elderly with chronic illnesses. IEEE Sens. J. 17(17):5694–5704, 2017.

    Article  Google Scholar 

  60. D’monte, S., and Panchal, D., Data mining approach for diagnose of anxiety disorder. Int. Conf. Comput. Commun. Autom. (ICCCA).;124–127, 2015.

  61. Tovar, D., Cornejo, E., Xanthopoulos, P., Guarracino, M. R., and Pardalos, P. M., Data mining in psychiatric research. Psychiatr. Disord. 829:593–603, 2012.

    Article  CAS  Google Scholar 

  62. Lyalina, S., Percha, B., Lependu, P., Iyer, S. V., Altman, R. B., and Shah, N. H., Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records. J. Am. Med. Inform. Assoc. 20:297–305, 2013.

    Article  Google Scholar 

  63. Panagiotakopoulos, T. C., Lyras, D. P., Livaditis, M., Sgarbas, K. N., Anastassopoulos, G. C., and Lymberopoulos, D. K., A contextual data mining approach toward assisting the treatment of anxiety disorders. IEEE Trans. Inf. Technol. Biomed. 14(3):567–581, 2010.

    Article  PubMed  Google Scholar 

  64. Ince, N. F., Goksu, F., Pellizzer, G., Tewfik, A., and Stephane, M., Selection of spectro-temporal patterns in multichannel MEG with support vector machines for schizophrenia classification. 2008 30th Annu. Int. Conf. IEEE Eng. Med. Biol Soc. ;3554–3557, 2008.

  65. Gangwar, M., Mishra, R. B., and Yadav, R. S., Application of decision tree method in the diagnosis of neuropsychiatric diseases. Asia-Pacific World Congr Comput Sci Eng. ;1–8, 2014.

  66. GeethaRamani, R., and Sivaselvi, K., Data mining technique for identification of diagnostic biomarker to predict schizophrenia disorder. 2014 IEEE Int. Conf. Comput. Intell. Comput. Res. ;1–8, 2014.

  67. Lanata, A., Greco, A., Valenza, G., and Scilingo, E. P., A pattern recognition approach based on electrodermal response for pathological mood identification in bipolar disorders. ICASSP, IEEE Int. Conf. Acoust. Speech Sign. Process ;3601–3605, 2014.

  68. Thongkam, J., and Sukmak, V., Enhancing decision tree with adaboost for predicting schizophrenia readmission. Adv. Mater. Res. 931:1467–1471, 2014.

    Article  Google Scholar 

  69. Castaldo, R., Xu, W., Melillo, P., Pecchia, L., Santamaria, L., and James, C., Detection of mental stress due to oral academic examination via ultra-short-term HRV analysis. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS. ;3805–3808, 2016.

  70. Azar, G., Gloster, C., El-Bathy, N., Yu, S., Neela, R. H, and Alothman, I., Intelligent data mining and machine learning for mental health diagnosis using genetic algorithm. IEEE Int. Conf. Electro. Inf. Technol.;201–206, 2015.

  71. Hadzic, M., Hadzic, F., and Dillon, T. S., Domain driven tree mining of semi-structured mental health information. Data Min. Bus. Appl. 2009;127–141.

  72. Nguyen, T., O’Dea, B., Larsen, M., Phung, D., Venkatesh, S., and Christensen, H., Using linguistic and topic analysis to classify sub-groups of online depression communities. Multimed. Tools Appl. 76(8):10653–10676, 2017.

    Article  Google Scholar 

  73. Cairney, J., Veldhuizen, S., Vigod, S., Streiner, D. L., Wade, T. J., and Kurdyak, P., Exploring the social determinants of mental health service use using intersectionality theory and CART analysis. J. Epidemiol. Commun. Health. 68(2):145–150, 2014.

    Article  Google Scholar 

  74. Barros, J., Morales, S., Echávarri, O., García, A., Ortega, J., Asahi, T. et al., Suicide detection in Chile: Proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders. Rev. Bras. Psiquiatr. 39(1):1–11, 2017.

    Article  PubMed  Google Scholar 

  75. Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK, Trivedi MH, et al. Cross-trial prediction of treatment outcome in depression: A machine learning approach. Lancet Psychiatr. 2016;3(3):243–250.

  76. 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.

    Google Scholar 

Download references

Acknowledgements

This research has been partially supported by the European Commission and the Ministry of Industry, Energy and Tourism under the project AAL-20125036 named “Wetake Care: ICT- based Solution for (Self-) Management of Daily Living”.

Author information

Authors and Affiliations

  1. Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain

    Alonso Susel Góngora, Isabel de la Torre-Díez, Miguel López-Coronado & Diego Calvo Barreno

  2. Bretagne Loire and Nantes Universities, UMR 6164, IETR Polytech Nantes, Nantes, France

    Sofiane Hamrioui

  3. Nozaleda and Lafora Mental Health Clinic, C/ José Ortega Y Gasset, 44, 28006, Madrid, Spain

    Lola Morón Nozaleda

  4. Psiquiatry Service, Hospital Zamora, Hernán Cortés, Zamora, Spain

    Manuel Franco

Authors
  1. Alonso Susel Góngora
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Isabel de la Torre-Díez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sofiane Hamrioui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Miguel López-Coronado
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Diego Calvo Barreno
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lola Morón Nozaleda
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Manuel Franco
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Isabel de la Torre-Díez.

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

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

Alonso, S.G., de la Torre-Díez, I., Hamrioui, S. et al. Data Mining Algorithms and Techniques in Mental Health: A Systematic Review. J Med Syst 42, 161 (2018). https://doi.org/10.1007/s10916-018-1018-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-018-1018-2

Keywords

Search

Navigation