Abstract
Nowadays, a lot of people have the oral health problems due to continuous changes in the lifestyle such as the person's speech which can be affected by the malocclusion in teeth and the crooked teeth. The dental problems can cause cavity and bacterial infection. The dental and speech problems mostly can be related to the Alzheimer disease, and cognitive changes. Therefore, the dental information is collected from patients and analyzed by applying intelligent machine learning techniques. The gathered dental information is normalized by standardized min max approach. Further, different statistical parameters are derived which are huge in dimension. The optimal features are selected using grey wolf optimized approach. The method effectively selects the optimum dental features and the selected features are processed using bacterial optimized associative deep neural network. The network collects the Alzheimer disease features and compare them with the collected dental features to establish the brain related issues with dental features. The efficiency of the system is evaluated using experimental results and discussion. Thus, the introduced intelligent bacterial optimized associative deep neural network recognizes the relationship up to 98.98% of accuracy which is the maximum accuracy compared to other methods. Further, IBADNN-based Alzheimer detection system approach attains maximum predicting and selecting disease features (precision 98.65% and recall 99.03%) whereas other approaches such as OLVQ (precision 95.03% and recall 96.23%), HACANN (precision 96.36% and recall 96.91%) and GCNN (precision 97.47% and recall 97.512%) and attains low predicting and selecting accuracy.