An Oral Healthcare Recommendation Framework Using Lion Inspired Feature Optimization and SVM Classification

Abstract

Oral health care is indispensable for patients with insulin resistance. This research work presents a novel framework for oral implant recommendation for insulin resistant patients. This framework recommends optimal implant types and customized preoperative strategies which are contemplated for such patients. This framework integrates a synthetic patient data modelling with more clinically significant features like HbA1c, bone density and glycemic control indicators. 3000 data which mimics the clinical data is generated and with which the model is trained. The features are optimized using a Lion’s Pride Inspired Algorithm (LPIA) which imitates the behavioural traits of Lions in their pride. The method of elitism is adopted for obtaining the optimal solution set. The classification is done by using Support Vector Machine. This combo demonstrated a strong performance with LPIA optimized feature space achieving a maximum classification of 81% and F1-weighted score up to 0.31. The ROC analysis was also performed for the implant types like Zirconia which produced AUC scores above 0.90 which validates the discriminatory capacity of the proposed framework. In addition, the clinical recommendation regarding the implant timing, glycemic management were generated dynamically. These results demonstrate the capability of the proposed framework as an intelligent, interpretable and patient specific decision support tool for dental implant planning in diabetic care.

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