Earticle

초록

영어

Heart disease is a major cause of mortality in the world that is in dire need of accurate, interpretable predictive measures that could be utilized to manage it proactively. The writer of this paper proposes an Explainable AI (XAI) Ensemble Machine Learning model to predict heart disease using an 1,025 patient record dataset. To achieve methodological rigor and generalization, 5- Fold Stratified Cross-Validation (CV) was used to evaluate all models, such as LightGBM and Random Forest. LightGBM model was stable and better in performance as it had Mean CV Accuracy of ±0.9620 ±0.0178. Integration of XAI (SHAP/LIME) is the means of creating clinical trust; analysis has confirmed maximum heart rate (thalach) and type of chest pain (cp) as medically significant characteristics. This framework supports the sustainable smart city healthcare through a highly transparent decision-support system, which manages the resources in optimizing scalable public health programs.

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. METHODOLOGY
A. Dataset and Preprocessing
B. Models and validation of Machine Learning
C. Model Training and Hyperparameter Configuration
D. Explainable AI (XAI)
E. Performance Evaluation
IV. RESULTS
A. Result of Random Forest Model
B. Result of ExtraTrees Model
C. Result of SVM Model
D. Result LightGBM Model
V. CONCLUSION
REFERENCES

저자

• Mehak Mussarat [ Department of Computer Science, Green International University Lahore, Pakistan ]
• Abdul Hannan Khan [ Department of Computer Science, Green International University Lahore, Pakistan ]
• Amna Ishtiaq [ Department of Computer Science, Green International University Lahore, Pakistan ]
• Roshaan Fatima [ School of Computing, Horizon University College, Ajman, UAE ]
• Hussain Dawood [ School of Computing, Horizon University College, Ajman, UAE ]
• Muhammad Adnan Khan [ School of Computing, Horizon University College, Ajman, UAE ] Corresponding Author

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