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

Area of Science:

• Data Science
• Machine Learning
• Mental Health Research

Background:

• Correlation analysis is vital for understanding attribute relevance in predictive modeling.
• Identifying key factors in mental health is crucial for accurate patient classification.
• Previous studies have explored attribute relevance but often lack integration with diverse data types.

Purpose of the Study:

• To leverage correlation analysis and machine learning to identify significant attributes for mental health status classification.
• To explore relationships within datasets encompassing depressive disorder symptoms, weather conditions, and physiological sensor readings.
• To enhance the accuracy of mental health predictions through attribute relevance identification.

Main Methods:

• Utilized correlation analysis, including Pearson's product moment correlation, within the Weka software.
• Applied various machine learning classification algorithms to analyze datasets.
• Integrated datasets related to depressive disorder symptoms, weather, and physiological sensor data.

Main Results:

• Identified notable correlations between weather attributes and bipolar disorder patient data.
• Discovered significant relationships between features from physiological sensor data and emotional states.
• Demonstrated the utility of correlation analysis in uncovering relevant mental health indicators.

Conclusions:

• Correlation analysis combined with machine learning effectively identifies critical attributes for mental health classification.
• The study highlights the influence of environmental and physiological factors on mental health and emotional states.
• Findings provide a foundation for developing more accurate and data-driven mental health diagnostic tools.