The document discusses a study aimed at predicting heart disease by integrating mental health indicators with traditional cardiovascular risk factors using machine learning models, particularly focusing on age and gender distinctions. It highlights the challenges faced by low and middle-income countries in accessing advanced diagnostic tools and emphasizes the importance of considering psychological factors in predictive models. The study employs various machine learning classifiers, aiming to enhance predictive accuracy and improve healthcare access through future developments such as wearable device integration.

1. AGENDA
1. Introduction
2. Literature Review
3. Research Objective
4. Proposed Methodology
5. Result Analysis
6. Conclusion
7. References

2. INTRODUCTION
 Cardiovascular diseases (CVDs) are the leading global cause of death,
responsible for 17.9 million deaths annually [1].
 Low and middle-income countries face critical challenges due to limited
healthcare resources.
 Advanced diagnostic tools (e.g., angiography, ECG) are often inaccessible due to
high costs and need for specialized professionals [2].
 Traditional prediction models focus only on physical factors (e.g., cholesterol,
blood pressure, age) [3].
 Mental health factors like stress, sleep duration, and working hours, which impact
cardiovascular health, are often overlooked [2].

3. INTRODUCTION
 Machine learning provides a cost-effective approach to predicting heart disease
risks [4].
 Study integrates psychological factors like stress, work hours, and sleep into
predictive models.
 Cleveland Heart Disease dataset enriched with synthetic mental health features for
analysis.
 Personalized models based on age (<40 vs. ≥40) and gender were explored.

4. LITERATURE REVIEW
• Mohan et al. [1], Highlighted the potential of hybrid machine learning models,
combining physical and psychological parameters, to improve predictive accuracy in
heart disease.
• Subahi et al. [5] Proposed the integration of stress levels, sleep patterns, and lifestyle
factors into datasets to enhance the prediction of heart disease.
• Yaqoob et al. [6], Identified fasting blood sugar as a critical predictor of heart disease,
but pointed out the limitations of models that exclude multi-dimensional health
factors, particularly psychological ones.
• Soni et al. [7] Demonstrated that advanced ML algorithms like Random Forest and
XGBoost outperform traditional models in predictive accuracy, effectively capturing
non-linear relationships between variables, but face challenges in model
explainability for clinical acceptance.

5. RESEARCH OBJECTIVE
 Integrates mental health indicators into heart disease prediction models using
the Cleveland Heart Disease dataset.
 Develops personalized machine learning models considering both physical and
mental health factors.
 Tailors models for specific age groups (under 40 and 40 or older) and gender to
evaluate personalized vs. generalized predictions.
 Employs multiple ML classifiers: Random Forest, XGBoost, KNN, SVM,
Logistic Regression, Decision Tree, and Naive Bayes.

6. PROPOSED METHODOLOGY
• Dataset Details: Cleveland Heart Disease Dataset (303 records, UC Irvine ML
Repository) [8].
• Attributes: Age, Sex, Cholesterol, Blood Pressure, Exercise-Induced Angina, etc.
• Synthetic Features Added: Stress Level (1–10), Sleep Duration (4–9 hours/day),
Work Hours (20–70 hours/week).
• Age Group: Young (<40 years) and Old (≥40 years)
• Data Pre-processing: Converted categorical variables (e.g., Sex, Chest Pain Type)
into numeric.
• Handled missing values via imputation or exclusion.
• Normalized data for scale-sensitive models (e.g., XGBoost, KNN).
• Feature Engineering: Key features identified using Random Forest importance.
• Experimented with top 2 and top 4 features for better performance.

7. PROPOSED METHODOLOGY
• Model Development:
• ML Models: Decision Tree, Random Forest, KNN, SVM, Logistic Data split
into 70% training and 30% testing for fair evaluation.
• Age Group-Based Analysis:
• Separate evaluation for Young (<40) and Old (≥40) groups.
• Analyzed performance patterns and age-related feature impact

8. MODELS USED FOR HEART DISEASE PREDICTION:
Decision Tree:
Splits data into branches based on feature values.
Advantages: Easy to interpret and visualize, straightforward
decision-making.
Disadvantages: Prone to overfitting and high variance without pruning.
Random Forest:
Ensemble model that averages multiple decision trees for predictions.
Advantages: Reduces overfitting, robust with noisy data, better generalization.
Disadvantages: Slower predictions and increased complexity with more trees.
K-Nearest Neighbors (KNN):
Classifies data points based on the majority class of their nearest neighbors.
Advantages: Simple and effective for small datasets, intuitive approach.
Disadvantages: Computationally intensive for large datasets, sensitive to scaling.

9. MODELS USED FOR HEART DISEASE PREDICTION:
Support Vector Machine (SVM):
Finds the best hyperplane to separate data classes in high-dimensional space.
Advantages: Effective for high-dimensional data, robust with a clear margin of
separation.
Disadvantages: High computational cost, slow training for large datasets.
Logistic Regression:
Predicts binary outcomes based on a logistic function.
Advantages: Easy to implement and interpret, well-suited for binary classification.
Disadvantages: Assumes linearity between features and log odds, which may not hold
for complex relationships.
XGBoost :
Optimized gradient boosting framework that builds sequential trees to correct errors.
Advantages: High accuracy, reduces overfitting with regularization.
Disadvantages: Computationally expensive, requires significant training time.

10. R E S U L T A N A L Y S I S

11. CONCLUSION
• The study integrates mental health indicators (stress, sleep, working hours) with
traditional cardiovascular risk factors to predict heart disease, enhancing model
accuracy across different demographic groups..
• ).
• Future research will focus on multi-modal models, wearable device integration for
real-time monitoring, and mobile applications to improve early diagnosis and
healthcare access, particularly in low-resource setting

12. REFERENCES
[1 [7] P. Soni and R. Sharma, "Deep Learning Models for Heart Disease
Prediction: A Review," Computers in Biology and Medicine, vol. 147, p.
105436, 2022.
[8] Psychogyios, K., Ilias, L., Askounis, D.: Comparison of missing data
imputation methods using the Framingham heart study dataset. In: 2022
IEEE-EMBS International Conference on Biomedical and Health
Informatics (BHI), pp. 1–5 (2022)