Abstract: Machine learning determines patterns from data to expedite the process of decision making. Fact-based decisions and data-driven decisions are specified by the industry specialist. Due to the continuous growth of machine language models in healthcare, they are breeding continuous complexity and black boxes in ML models. To make the ML model crystal clear and authentically explainable, AI accession came in prevalence. This research scrutinizes the explainable AI and capabilities in the Indian healthcare system to detect diabetes. LIME and SHAP are two libraries and packages that are used to implement explainable AI. The intimated base amalgamates the local and global interpretable methods, which enhances the crystallinity of the complex model and obtains intuition into the equity from the complex model. Moreover, the obtained intuition could also boost clinical data scientists to plan a more felicitous composition of computer-aided diagnosis. Importance of XAI to forecast stubborn disease. In this case, of stubborn diabetes, the correlation between plasma versus insulin, age versus pregnancies, class (diabetic and nondiabetic) versus plasma glucose persisted with a strong relationship. The PIDD (PIMA Indian Diabetic Data set) with the SHAP value is used for concise dependency, and LIME is applicable when anchors and importance of features are both required simultaneously. Dependency plots help physicians visualize independent relationships with predicted disease. To identify dependencies of different attributes, a correlation heatmap is used. From an academic perspective, XAI is very indispensable to mature in the near future. To estimate the presentation of other applicable data set correspondence studies are very much apprenticed.

Rita Ganguly and Dharmpal Singh, “Explainable Artificial Intelligence (XAI) for the Prediction of Diabetes Management: An Ensemble Approach” International Journal of Advanced Computer Science and Applications(IJACSA), 14(7), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140717

@article{Ganguly2023,
title = {Explainable Artificial Intelligence (XAI) for the Prediction of Diabetes Management: An Ensemble Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140717},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140717},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {7},
author = {Rita Ganguly and Dharmpal Singh}
}