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RSS FeedsIJERPH, Vol. 15, Pages 2907: Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States (International Journal of Environmental Research and Public Health)

 
 

22 december 2018 07:00:01

 
IJERPH, Vol. 15, Pages 2907: Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States (International Journal of Environmental Research and Public Health)
 




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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25 viewsCategory: Medicine, Pathology, Toxicology
 
IJERPH, Vol. 15, Pages 2909: Healthy Nutrition and Physical Activity in Childcare: Views from Childcare Managers, Childcare Workers and Parents on Influential Factors (International Journal of Environmental Research and Public Health)
IJERPH, Vol. 15, Pages 2906: Analyzing Policies Through a DOHaD Lens: What Can We Learn? (International Journal of Environmental Research and Public Health)
 
 
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