Statistical Analysis and Machine Learning Amalgamated Convolution Neural Network (CNN) Approach on Correlating the Dental Plaque with Cardiac Illness
Keywords:
Convolution Neural Network (CNN), Statistical examination, Data analysis, Dental health, Cardiac illnessAbstract
Dental plaque (DP) is a thin sticky film that coats the teeth and contains bacteria. The DP is caused due to the consumption of food with high percentage of carbohydrates, sugary foods and drinks, and fatty foods. The bacteria that feeds on the sugar of the foods produces acid which forms DP. However these bacteria reach the bloodstream, digestive and respiratory tracts can cause some serious diseases such as heart diseases, cancer, tumors etc. The present paper is focussed on finding the correlation of DP with other seismic factors and developing machine learning models to predict cardiovascular diseases (CVD). In achieving the aim of the paper, the analysis is carried out in three phases. First phase involves the development of a convolution neural network (CNN) model to identify and categorize DP on the basis of the thickness of plaque deposition. The second phase involves correlating DP with other seismic factors. Those seismic factors that showed positive and significant relation with DP are chosen as parameters for developing the ML models. Finally, ML models are developed using the six different algorithms namely XGBoost, Logistic Regression, Support Vector Machines, Random Forest, Decision Tree and k-Nearest Neighbor. The ML model built by XGBoost showed the best performance on the basis of training and testing accuracy, false negative and true positive values. The strength of the proposed approach is its practical applicability
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