Published Paper Details:

Gangrene is a severe medical condition characterized by the death of body tissues due to a lack of blood supply or infection. Timely and accurate prediction of gangrene can significantly impact patient outcomes and guide appropriate medical interventions. This paper provides an overview and analysis of machine learning-based prediction models for gangrene. We begin by discussing the clinical significance of gangrene, its causes, and the challenges associated with its prediction. We emphasize the need for early detection and intervention to prevent further tissue damage and potential complications.Various machine learning algorithms, including logistic regression, K-Nearest Neighbors, support vector machine, naive bayes, decision trees and random forests, are examined for their application in gangrene prediction. We explore the features and data sources commonly used in these models, such as patient Diabetis, blood pressure, heart rate, respiratory rate, body temperature, skin condition and line of demarkcation. Additionally, we discuss the importance of feature selection and extraction techniques to enhance the predictive performance of the models. Data preprocessing steps, including missing data imputation, outlier detection, and normalization, are addressed to ensure data quality and model robustness. We delve into the evaluation metrics employed to assess the performance of gangrene prediction models, such as accuracy.