Abstract:
Now a day’s dental disease is the major health problem in Bangladesh. So dental care is
important to most people in our country. But the cost of dental care services is increasing
day by day. We will predict the risk of dental disease with machine learning. We identify
the most common disease among people, consult with a dentist about those diseases,
reading-related journals, and online articles, we find out the habitats that cause dental
disease. Then we collect data based on those factors, such as age, brush before sleep, brush
after eating morning, eating chocolates, soft drinks, betel leaf/nut, etc. We collect data from
both those who have already a disease and those who don’t. We have two outcomes. One is ‘Yes’
meaning they have dental disease and another is ‘No’ means they don’t have dental disease.
We apply machine-learning algorithms to our processed dataset. Recently machine
learning, artificial intelligence, and deep learning used in various predictions and detection
systems. We use k-nearest neighbor (KNN), logistic regression, support vector machine
(SVM), naïve Bayes, random forest, adaptive boosting (ADA boosting), decision tree,
multilayer perceptron (MLP-ANN), Linear Discriminant Analysis (LDA), and gradient
boosting classifier. In our work, we use those factors answer as input and after processing
and applying the algorithm, we find out addicted or not addicted as our output with the
accuracy of 95.89% on the logistic regression algorithm.
