Developing an Explainable Artificial Intelligence System for the Mobile-Based Diagnosis of Febrile Diseases Using Random Forest, LIME, and GPT
10.4258/hir.2025.31.2.125
- Author:
Kingsley F. ATTAI
1
;
Constance AMANNAH
;
Moses EKPENYONG
;
Daniel E. ASUQUO
;
Oryina K. AKPUTU
;
Okure U. OBOT
;
Peterben C. AJUGA
;
Jeremiah C. OBI
;
Omosivie MADUKA
;
Christie AKWAOWO
;
Faith-Michael UZOKA
Author Information
1. Department of Mathematics and Computer Science, Ritman University, Ikot Ekpene, Nigeria
- Publication Type:Original Article
- From:Healthcare Informatics Research
2025;31(2):125-135
- CountryRepublic of Korea
- Language:English
-
Abstract:
Objectives:This study proposes a mobile-based explainable artificial intelligence (XAI) platform designed for diagnosing febrile illnesses.
Methods:We integrated the interpretability offered by local interpretable model-agnostic explanations (LIME) and the explainability provided by generative pre-trained transformers (GPT) to bridge the gap in understanding and trust often created by machine learning models in critical healthcare decision-making. The developed system employed random forest for disease diagnosis, LIME for interpretation of the results, and GPT-3.5 for generating explanations in easy-to-understand language.
Results:Our model demonstrated robust performance in detecting malaria, achieving precision, recall, and F1-scores of 85%, 91%, and 88%, respectively. It performed moderately well in detecting urinary tract and respiratory tract infections, with precision, recall, and F1-scores of 80%, 65%, and 72%, and 77%, 68%, and 72%, respectively, maintaining an effective balance between sensitivity and specificity. However, the model exhibited limitations in detecting typhoid fever and human immunodeficiency virus/acquired immune deficiency syndrome, achieving lower precision, recall, and F1-scores of 69%, 53%, and 60%, and 75%, 39%, and 51%, respectively. These results indicate missed true-positive cases, necessitating further model fine-tuning. LIME and GPT-3.5 were integrated to enhance transparency and provide natural language explanations, thereby aiding decision-making and improving user comprehension of the diagnoses.
Conclusions:The LIME plots revealed key symptoms influencing the diagnoses, with bitter taste in the mouth and fever showing the highest negative influence on predictions, and GPT-3.5 provided natural language explanations that increased the reliability and trustworthiness of the system, promoting improved patient outcomes and reducing the healthcare burden.
