Data analysis on Mobile Healthcare Initiative in Hong Kong using ML | thinkswithyou
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Table of Contents
1. Introduction
Overview of the 2008–2018 Mobile Healthcare Initiative in Hong Kong
Goal: Forecasting MMSE Class Categories for Dementia Evaluation
2. Dataset Description
Collection Period: 2008–2018
12 Columns, 2229 Rows
Key Variables: Age, Height, Weight, Education, Mobility, Mini Nutritional Assessment (MNA)
Prediction Target: Mini Mental State Examination (MMSE) Class Categories
Initial Dataset Overview (Figure 1)
3. Data Pre-Processing
Removal of 'X' Identifier Variable
Handling Missing Values
No Encoding Needed for Categorical Variables
Importance of Pre-Processing for Model Quality
4. Exploratory Data Analysis (EDA)
Significant Correlations between Variables and MMSE Scores
Visual Representations: Correlation Matrix (Figure 3), Class Distribution (Figure 4), Age-Education Boxplot (Figure 5), Scatterplot (BMI vs MMSE) (Figure 6)
5. Prediction Modeling
Utilizing Random Forest and Logistic Regression
Choice Justification: Random Forest's Flexibility, Logistic Regression's Simplicity
Train-Test Split: 80:20 Ratio
6. Results and Models’ Comparison
Confusion Matrix Analysis for Random Forest and Logistic Regression
Accuracy Rates: Random Forest (84.78%), Logistic Regression (83.48%)
Random Forest Selected as Preferred Model
7. Discussion
Significance of 'X' Variable Removal and Data Completeness
Key Findings from EDA: Age-Mental State Connection, Education-Mental State Relationship
Positive Correlation between BMI and MMSE Scores
Implications for Mental Health and Aging Research
8. Conclusion
Application of Data Analysis and Prediction Modeling for Elderly Mental Health
Insights into Impact of Age, Education, Mobility, and Nutrition on MMSE Scores
Random Forest Model Proven Useful for MMSE Class Prediction
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