Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States

Overview

Journal Int J Environ Res Public Health

Publisher MDPI

Specialties Environmental Health
Public Health

Date 2018 Dec 22

PMID 30572595

Citations 17

Authors

Sunil Kumar

Ilyoung Chong

Affiliations

Soon will be listed here.

Abstract

Correlation analysis is an extensively used technique that identifies interesting relationships in data. These relationships help us realize the relevance of attributes with respect to the target class to be predicted. This study has exploited correlation analysis and machine learning-based approaches to identify relevant attributes in the dataset which have a significant impact on classifying a patient's mental health status. For mental health situations, correlation analysis has been performed in Weka, which involves a dataset of depressive disorder symptoms and situations based on weather conditions, as well as emotion classification based on physiological sensor readings. Pearson's product moment correlation and other different classification algorithms have been utilized for this analysis. The results show interesting correlations in weather attributes for bipolar patients, as well as in features extracted from physiological data for emotional states.

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References

Knott V, Mahoney C, Kennedy S, Evans K . EEG power, frequency, asymmetry and coherence in male depression. Psychiatry Res. 2001; 106(2):123-40. DOI: 10.1016/s0925-4927(00)00080-9. View

Ambroise C, McLachlan G . Selection bias in gene extraction on the basis of microarray gene-expression data. Proc Natl Acad Sci U S A. 2002; 99(10):6562-6. PMC: 124442. DOI: 10.1073/pnas.102102699. View

Bauman A, Sallis J, Dzewaltowski D, Owen N . Toward a better understanding of the influences on physical activity: the role of determinants, correlates, causal variables, mediators, moderators, and confounders. Am J Prev Med. 2002; 23(2 Suppl):5-14. DOI: 10.1016/s0749-3797(02)00469-5. View

Li J, Zhang Z, Rosenzweig J, Wang Y, Chan D . Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. Clin Chem. 2002; 48(8):1296-304. View

Wahlby U, Jonsson E, Karlsson M . Comparison of stepwise covariate model building strategies in population pharmacokinetic-pharmacodynamic analysis. AAPS PharmSci. 2003; 4(4):E27. PMC: 2751316. DOI: 10.1208/ps040427. View

Bulbena A, Pailhez G, Acena R, Cunillera J, Rius A, Garcia-Ribera C . Panic anxiety, under the weather?. Int J Biometeorol. 2005; 49(4):238-43. DOI: 10.1007/s00484-004-0236-0. View

Pantic M, Patras I . Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences. IEEE Trans Syst Man Cybern B Cybern. 2006; 36(2):433-49. DOI: 10.1109/tsmcb.2005.859075. View

Mitani H, Shirayama Y, Yamada T, Maeda K, Ashby Jr C, Kawahara R . Correlation between plasma levels of glutamate, alanine and serine with severity of depression. Prog Neuropsychopharmacol Biol Psychiatry. 2006; 30(6):1155-8. DOI: 10.1016/j.pnpbp.2006.03.036. View

van der Gaag M, Hoffman T, Remijsen M, Hijman R, de Haan L, van Meijel B . The five-factor model of the Positive and Negative Syndrome Scale II: a ten-fold cross-validation of a revised model. Schizophr Res. 2006; 85(1-3):280-7. DOI: 10.1016/j.schres.2006.03.021. View

10.

Albert P, Rosen L, Alexander Jr J, Rosenthal N . Effect of daily variation in weather and sleep on seasonal affective disorder. Psychiatry Res. 1991; 36(1):51-63. DOI: 10.1016/0165-1781(91)90117-8. View

11.

Huibers M, de Graaf L, Peeters F, Arntz A . Does the weather make us sad? Meteorological determinants of mood and depression in the general population. Psychiatry Res. 2010; 180(2-3):143-6. DOI: 10.1016/j.psychres.2009.09.016. View

12.

Choi K, Kang S . Occupational psychiatric disorders in Korea. J Korean Med Sci. 2011; 25(Suppl):S87-93. PMC: 3023353. DOI: 10.3346/jkms.2010.25.S.S87. View

13.

Burns M, Begale M, Duffecy J, Gergle D, Karr C, Giangrande E . Harnessing context sensing to develop a mobile intervention for depression. J Med Internet Res. 2011; 13(3):e55. PMC: 3222181. DOI: 10.2196/jmir.1838. View

14.

Zeng L, Shen H, Liu L, Wang L, Li B, Fang P . Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain. 2012; 135(Pt 5):1498-507. DOI: 10.1093/brain/aws059. View

15.

Park H, Heo J, Subramanian S, Kawachi I, Oh J . Socioeconomic inequalities in adolescent depression in South Korea: a multilevel analysis. PLoS One. 2012; 7(10):e47025. PMC: 3471941. DOI: 10.1371/journal.pone.0047025. View

16.

Hosseinifard B, Moradi M, Rostami R . Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Comput Methods Programs Biomed. 2012; 109(3):339-45. DOI: 10.1016/j.cmpb.2012.10.008. View

17.

Khodayari-Rostamabad A, Reilly J, Hasey G, de Bruin H, Maccrimmon D . A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder. Clin Neurophysiol. 2013; 124(10):1975-85. DOI: 10.1016/j.clinph.2013.04.010. View

18.

Park J, Han M, Park J, Ryu S . Prevalence of Depressive Symptoms and Related Factors in Korean Employees: The Third Korean Working Conditions Survey (2011). Int J Environ Res Public Health. 2016; 13(4):424. PMC: 4847086. DOI: 10.3390/ijerph13040424. View

19.

Jeong B, Veenstra G . The intergenerational production of depression in South Korea: results from a cross-sectional study. Int J Equity Health. 2017; 16(1):13. PMC: 5237188. DOI: 10.1186/s12939-016-0513-7. View

20.

Jarwar M, Kibria M, Ali S, Chong I . Microservices in Web Objects Enabled IoT Environment for Enhancing Reusability. Sensors (Basel). 2018; 18(2). PMC: 5856163. DOI: 10.3390/s18020352. View