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Predicted mental health and discovered its association with population density, income, water, and land features using machine learning models.

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Tekateka/Mental_Health_Predictor

 
 

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Mental Health Predictor

Background

  • Defining Mental illness
    • Disturbance of thought , experience, and emotion that causes functional impairment in people.
    • Interpersonal difficulty, limiting the ability to work and self destructive behavior.
  • In US nearly 1 in 5 adults aged 18 years or older (18.5%) have experienced mental illness (US Burden of Disease Collaborator, 2013).
  • Data science can help us to better understand and effectively implement treatments for mental health problems
  • Factors causing mental illness - anxiety, depression, biological, psychological and sociological (environment) approaches.

Project Focus

  • Our analysis project focuses on identifying factors associated with the prevalence of poor mental health in the US.
    • Population density, income, water and land features
  • Building a machine learning model that can predict mental health risk for an individual based on designated factors
  • What are the most and least significant factors (features) in predicting prevalence of poor mental health in the US?

Description of Data Sources

Tools/Resources

  • Creating ERD
  • Creating Database
    • SQLite
  • Analyzing Data
    • Pandas
  • Connecting to Database
  • Machine Learning
    • Imbalanced-learn
    • Scikit-Learn
    • Tensorflow
    • Dashboard
    • Tableau
    • Flask
    • HTML/CSS

Machine Learning Model

  • We used a binary outcome based on %poor mental health prevalence.
  • The binary outcome was calculated by median split:
    • The median % poor mental health of the 500 cities was 13.89%. So…
      • If a city < 13.89% poor mental health → “Good Mental Health”
      • If a city >= 13.89% poor mental health → “Bad Mental Health”
  • Features were log-transformed and scaled to bring them into a normal distribution
  • We tried logistic regression, support vector machines (1-3 kernels), decision tree, gradient tree boost (learning rates .05 - 1), random forest, and 1-2 layer deep learning
  • We used 10-fold cross-validation - i.e., 10 machine learning instances of randomly allocating 90% of data to training and 10% to testing. We averaged the performance across the 10 instances.

accuracy f

Summary of Findings

  • Multiple machine learning models were used and most of them provided about 80% accuracy in their mental health risk prediction.
  • With Random Forest model the most strongest feature in predicting poor mental health was Standard Deviation of Income.
  • This suggests that income inequality in a city most predicted the prevalence of poor mental health.

Limitations

  • Mental health data
    • Small sample
    • Limited availability
    • Subjective self-rating
  • Differences in time frame of the datasets

Recommendations for Further Analysis

  • Exploring data related to:
    • Impact of COVID-19
    • Climate
    • Affordable healthcare
    • Availability of mental healthcare providers
    • Data from wearable devices can be used to identify physiological markers associated with mental illness
    • Social media and internet activity can provide insight into a behavioral side of mental health

Presenation Slides

  • The presentation of the project will be found on a Google Slide Presenation, Here

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Predicted mental health and discovered its association with population density, income, water, and land features using machine learning models.

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