ChatGPT-Assisted Machine Learning for Chronic Disease Classification and Prediction: A Developmental and Validation
Sumira Abbas1, Mahwish Iftikhar2, Mian Mufarih Shah2
1Department of Pathology, Peshawar Medical College, Peshawar, PAK.
Insights
Machine learning models, including random forest, show promise for predicting chronic diseases like CKD and heart disease. ChatGPT assisted in optimizing these models for better accuracy and feature selection.
Area of Science:
- Medical Informatics
- Computational Biology
- Machine Learning in Healthcare
Background:
- Chronic diseases (CKD, CLD, TB, dementia, heart disease) are major global health issues.
- Early diagnosis and intervention are crucial for improving patient outcomes and reducing healthcare costs.
Purpose of the Study:
- To evaluate the efficacy of machine learning (ML) models in classifying and predicting chronic diseases.
- To explore the role of ChatGPT in enhancing ML model development for disease prediction.
Main Methods:
- A prospective observational study involving 260 patients across five chronic disease categories.
- Utilized logistic regression, random forest, and support vector machines, with ChatGPT assisting in feature selection and hyperparameter optimization.
- Employed 10-fold cross-validation and evaluated models using accuracy, sensitivity, specificity, precision, recall, F1-score, and AUC-ROC.
Main Results:
- The random forest model achieved high accuracy in predicting CKD (85.3%) and heart disease (88.2%).
- Logistic regression effectively predicted TB (80.1%) and dementia (82.4%).
- Hemoglobin and erythrocyte sedimentation rate were identified as key predictive parameters.
Conclusions:
- Machine learning algorithms, particularly random forest, demonstrate significant potential for chronic disease classification and prediction.
- ChatGPT integration improved ML model development through optimized feature selection and hyperparameter tuning.
- Future research should prioritize external validation via multi-center studies and prospective clinical trials.
Abstract:
Background Chronic diseases such as chronic kidney disease (CKD), chronic liver disease (CLD), tuberculosis (TB), dementia, and heart disease are global health concerns of significant importance, representing major causes of morbidity and mortality worldwide. Early diagnosis and interventions are critical to improve patient outcomes and reduce healthcare costs. Methods This prospective observational study analyzed clinical data from 270 patients (calculated using G*Power 3.1.9.7 analysis (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany), α = 0.05, power = 0.80), with 260 (96.3%) completing the protocol. The cohort comprised 149 (55.2%) males and 121 (44.8%) females, distributed across CKD (n=55, 21.2%), CLD (n=52, 20.0%), TB (n=51, 19.6%), dementia (n=50, 19.2%), and heart disease (n=52, 20.0%). Three ML models were employed with ChatGPT version 3.5 assistance (OpenAI, San Francisco, CA, USA) in feature selection and hyperparameter optimization: logistic regression, random forest, and support vector machines. Model performance was evaluated using accuracy, sensitivity, specificity, precision, recall, F1-score, and AUC-ROC metrics. Ten-fold cross-validation was applied to ensure robustness. Results The random forest model demonstrated superior performance, achieving the highest accuracy in predicting CKD (47/55, 85.3%, p < 0.001, sensitivity 45/55, 82.5%, specificity 48/55, 87.2%) and heart disease (46/52, 88.2%, p < 0.001, sensitivity 45/52, 85.7%, specificity 47/52, 90.1%). Logistic regression effectively predicted TB (41/51, 80.1%, p < 0.01) and dementia (41/50, 82.4%, p < 0.01). Key predictive parameters included hemoglobin (median 10.2 g/dL, IQR 8.4-12.6) and erythrocyte sedimentation rate (median 42.0 mm/hr, IQR 20.0-65.0). Model validation showed high consistency, with positive acid-fast bacilli in 40/51 (78.4%) TB cases and characteristic radiological findings in 43/51 (84.3%) cases. Conclusion ML algorithms, particularly random forest, show promising potential in chronic disease classification and prediction. The integration of ChatGPT enhanced model development through optimized feature selection and hyperparameter tuning. Future research should focus on external validation through multi-center studies and prospective clinical trials.
