APPROACHES TO RISK ASSESSMENT AND EARLY HERNIA DETECTION USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Abstract

Hernia detection is one of the critical medical diagnostics, and hence, promising advancements are made in early prediction and risk assessment using AI and ML techniques. In this paper, different AI and ML models are assessed, ranging from deep learning to traditional techniques, classifying patients into high-risk or low-risk categories. It discusses performance of models like CNNs, SVM, RF, RNN, and ANN while dealing with medical images, the possibility of training more than one model for better accuracy. There are some challenges ahead for implementing AI in a clinical setting, such as the privacy and validation of data; this work points to future potential in hernia detection.

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