Hybrid Learning System-Based Dental Caries Detection in X-Ray Images: Comparing Accuracy with Support Vector Machine

The primary objective of this study is to conduct a comparison between the accuracy of Support Vector Machines(SVM) and a Novel Hybrid Learning System (Novel HLS) for the detection of dental caries in dental photosobtained from a dedicated dataset. In this investigation, a total of 86 samples were gathered and divided into twodistinct groups. Specifically, Group 1 comprised 43 samples that were processed using the Novel HLS approach,while Group 2 consisted of 43 samples that underwent processing with the SVM method. The dataset wasimported as per the research protocol, and the Novel HLS code was developed employing Google Colab software.To determine the sample size, an online statistical analysis tool was employed, aiming for an 80% pretest powerand an alpha value of 0.05. The sample size was calculated based on prior research findings. Results revealedthat SVM achieved an accuracy rate of 70.816%, while the novel HLS method demonstrated a significantlyhigher accuracy of 97.221%. A statistical significance level of 0.012 (P < 0.05) indicated that there exists anoteworthy disparity in accuracy between the two methods. The dataset substantiates the observation that theNovel HLS approach outperforms SVM by a significant margin in terms of its predictive capabilities for dentalcaries detection.