Predictive Modeling of Diseases with Explainable Artificial Intelligence Using LightGBM
Abstract views: 8
/ 
PDF downloads: 7
Keywords:
Disease Prediction, Machine Learning, AI-Driven Healthcare, SHAP, Decision Support SystemsAbstract
The continuous exploration of the intricate connections among symptoms, patient attributes,
and diseases within the intricate landscape of human health represents an ongoing pursuit. Data-driven
methodologies have ushered in novel opportunities for comprehending these intricate relationships.
Especially with the COVID-19 pandemic, the paradigms of disease understanding, diagnosis, and
treatment management have assumed unprecedented significance. This study, powered by LightGBM and
SHAP, has the potential to provide invaluable support to experts in decision support systems, early
diagnosis of diseases, personalized treatment plan applications, strengthening medical interventions with
case-oriented treatment predictions by producing advanced diagnosis and treatment strategies at
demographic scales and analyzing risk factors, developing evidence-based public health policies and
proactive health services, researchers. Furthermore, this research can be effectively leveraged in
epidemiological investigations to ascertain the correlations and emerging trends between various diseases
and the influencing health determinants all with an impressive 81% accuracy.
Downloads
References
Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337-339.
D. Dahiwade, G. Patle and E. Meshram, "Designing Disease Prediction Model Using Machine Learning Approach," 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2019, pp. 1211-1215, doi: 10.1109/ICCMC.2019.8819782.
Politis, M., Wu, K., Molloy, S., G. Bain, P., Chaudhuri, K. R., & Piccini, P. (2010). Parkinson's disease symptoms: the patient's perspective. Movement Disorders, 25(11), 1646-1651.
Zhou, X., Menche, J., Barabási, A. L., & Sharma, A. (2014). Human symptoms–disease network. Nature communications, 5(1), 4212.
Cohen, S., & Williamson, G. M. (1991). Stress and infectious disease in humans. Psychological bulletin, 109(1), 5.
Merriam, A. E., Aronson, M. K., Gaston, P., Wey, S. L., & Katz, I. (1988). The psychiatric symptoms of Alzheimer's disease. Journal of the American Geriatrics Society, 36(1), 7-22.
Zoabi, Y., Deri-Rozov, S., & Shomron, N. (2021). Machine learning-based prediction of COVID-19 diagnosis based on symptoms. npj digital medicine, 4(1), 3.
Huang, S., Yang, J., Fong, S., & Zhao, Q. (2021). Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives. International journal of biological sciences, 17(6), 1581.
Alimadadi, A., Aryal, S., Manandhar, I., Munroe, P. B., Joe, B., & Cheng, X. (2020). Artificial intelligence and machine learning to fight COVID-19. Physiological genomics, 52(4), 200-202.
Keshavarzi Arshadi, A., Webb, J., Salem, M., Cruz, E., Calad-Thomson, S., Ghadirian, N., ... & Yuan, J. S. (2020). Artificial intelligence for COVID-19 drug discovery and vaccine development. Frontiers in Artificial Intelligence, 65.
Mak, K. K., & Pichika, M. R. (2019). Artificial intelligence in drug development: present status and future prospects. Drug discovery today, 24(3), 773-780.
Waldstein, S. M., Vogl, W. D., Bogunovic, H., Sadeghipour, A., Riedl, S., & Schmidt-Erfurth, U. (2020). Characterization of drusen and hyperreflective foci as biomarkers for disease progression in age-related macular degeneration using artificial intelligence in optical coherence tomography. JAMA ophthalmology, 138(7), 740-747.
Jamshidi, M., Lalbakhsh, A., Talla, J., Peroutka, Z., Hadjilooei, F., Lalbakhsh, P., ... & Mohyuddin, W. (2020). Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment. Ieee Access, 8, 109581-109595.
Schork, N. J. (2019). Artificial intelligence and personalized medicine. Precision medicine in Cancer therapy, 265-283.
Belić, M., Bobić, V., Badža, M., Šolaja, N., Đurić-Jovičić, M., & Kostić, V. S. (2019). Artificial intelligence for assisting diagnostics and assessment of Parkinson’s disease—A review. Clinical neurology and neurosurgery, 184, 105442.
Dey, S. K., Rahman, M. M., Siddiqi, U. R., & Howlader, A. (2020). Analyzing the epidemiological outbreak of COVID‐19: A visual exploratory data analysis approach. Journal of medical virology, 92(6), 632-638.
Disease Symptoms and Patient Profile Dataset. https://www.kaggle.com/datasets/uom190346a/disease-symptoms-and-patient-profile-dataset
Access Date : 12 Sep 2023.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.
Al Daoud, E. (2019). Comparison between XGBoost, LightGBM and CatBoost using a home credit dataset. International Journal of Computer and Information Engineering, 13(1), 6-10.
Shapley, L. S. (1953). A value for n-person games.
Parsa, A. B., Movahedi, A., Taghipour, H., Derrible, S., & Mohammadian, A. K. (2020). Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident Analysis & Prevention, 136, 105405.
