Explainability of artificial neural network in predicting career fulfilment among medical doctors in developing...

Career fulfilment among medical doctors is crucial for job satisfaction, retention, and healthcare quality, especially in developing nations with challenging healthcare systems. Traditional career guidance methods struggle to address the complexities of career fulfilment. While recent advancements in machine learning, particularly Artificial Neural Network (ANN) models, offer promising solutions for personalized career predictions, their applicability, interpretability, and impact remain challenging.

This study explores the applicability, explainability, and implications of ANN models in predicting career fulfillment among medical doctors in developing nations, considering socio-economic, psychological, and professional factors. Box plots visualized data distribution, while Heatmaps assessed data intensity and relationships. Matthew's correlation coefficient and Taylor's chart were used to evaluate model performance. Input feature contributions to ANN predictions were analyzed using permutation importance, SHAP, LIME, and Williams plots. The model was tested on a dataset tailored to medical professionals in Nigeria and China, with evaluation metrics including Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R² Score.

The ANN model demonstrates strong predictive accuracy, capturing relationships between input factors and outcomes. For Chinese doctors, it achieved an MSE of 0.0004 and R² of 0.9994, while for Nigerian doctors, it recorded an MSE of 0.0003 and R² of 0.9998. Key factors for Chinese doctors' satisfaction were IF1 and IF2, while EF1 and EF3 were crucial in preventing dissatisfaction. For Nigerian doctors, IF2 and IF3 drove satisfaction, while EF1 and EF4 were significant in avoiding dissatisfaction.

The results highlights the ANN model's effectiveness in predicting career fulfillment among medical doctors in developing nations, offering a valuable tool for career guidance, policymaking, and improving job satisfaction, retention, and healthcare quality.