• Vishal Patel
• Kumar Shukla
• Fnu Saba Sultana

The increasing adoption of machine learning (ML) in healthcare promises to revolutionize diagnosis, treatment, and overall patient care. However, the "black-box" nature of many powerful ML models can limit their clinical acceptance due to a lack of transparency and interpretability. This study investigates the performance and interpretability of various machine learning models suitable for analyzing healthcare data. We compare the accuracy and efficiency of these models with a focus on their ability to provide insights into the decision-making process. The study explores interpretable models such as decision trees, rule-based systems, and some recent advancements like Local Interpretable Model-Agnostic Explanations (LIME) for complex models. Through a comparative analysis on real-world healthcare datasets, we aim to identify the optimal balance between model performance and interpretability for specific healthcare applications. This work can guide healthcare professionals and researchers in selecting suitable interpretable models for analyzing their data while ensuring accurate and explainable results.

"Machine Learning Models for Analyzing Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.i45-i52, April-2024, Available :http://www.jetir.org/papers/JETIR2404807.pdf

"Machine Learning Models for Analyzing Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppi45-i52, April-2024, Available at : http://www.jetir.org/papers/JETIR2404807.pdf