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.

如今，由于生活方式的不断变化（例如人的言语），很多人都存在口腔健康问题，这可能会受到牙齿错合和弯曲牙齿的影响。牙齿问题可能导致蛀牙和细菌感染。牙齿和言语问题大多与阿尔茨海默氏病和认知变化有关。因此，从患者那里收集牙科信息并通过应用智能机器学习技术进行分析。所收集的牙齿信息通过标准化的最小最大方法进行标准化。此外，导出了巨大的不同统计参数。使用灰太狼优化方法选择最佳特征。该方法有效地选择了最佳的牙齿特征，并使用细菌优化的关联深度神经网络对选定的特征进行处理。该网络收集阿尔茨海默氏病特征并将其与收集的牙齿特征进行比较，以建立与牙齿特征有关的大脑相关问题。使用实验结果和讨论来评估系统的效率。因此，引入的智能细菌优化关联深度神经网络可以识别高达98.98％的精度关系，这是与其他方法相比最大的精度。此外，基于IBADNN的Alzheimer检测系统方法可实现最大程度的预测和选择疾病特征（精度为98.65％，召回率为99.03％），而其他方法如OLVQ（精度为95.03％，召回率为96.23％），HACANN（精度为96。