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. 2018 Dec 19;15(12):2907.
doi: 10.3390/ijerph15122907.

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

Sunil Kumar  1 , Ilyoung Chong  2
Affiliations

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

Sunil Kumar et al. Int J Environ Res Public Health. .

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.

Keywords: correlation analysis; data analytics; health care; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Correlation based on direction, form, and dispersion strength.
Figure 2
Figure 2
Methodology to identify strong predictor attributes.
Figure 3
Figure 3
Process flow of Data Analytics: (a) Emotion detection. (b) Identifying depressive disorder severity.
Figure 4
Figure 4
Scatter plot in Weka, of top-ranked weather parameters for Bipolar disorder.
Figure 5
Figure 5
Accuracies of prediction models with respect to stepwise feature selection.
See this image and copyright information in PMC

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