Regression Imputation and Optimized Gaussian Naïve Bayes Algorithm for an Enhanced Diabetes Mellitus Prediction Model

Abstract Diabetes mellitus (DM) is a category of metabolic disorders caused by high blood sugar. The DM affects human metabolism, and this disease causes many complications like Heart disease, Neuropathy, Diabetic retinopathy, kidney problems, skin disorder and slow healing. It is therefore essential to predict the presence of DM using an automated diabetes diagnosis system, which can be implemented using machine learning algorithms. A variety of automated diabetes prediction systems have been proposed in previous studies. Even so, the low prediction accuracy of DM prediction systems is a major issue. This proposed work developed a diabetes mellitus prediction system to improve the diabetes mellitus prediction accuracy using Optimized Gaussian Naive Bayes algorithm. This proposed model using the Pima Indians diabetes dataset as an input to build the DM predictive model. The missing values of an input dataset are imputed using regression imputation method. The sequential backward feature elimination method is used in this proposed model for selecting the relevant risk factors of diabetes disease. The proposed machine learning classifier named Optimized Gaussian Naïve Bayes (OGNB) is applied to the selected risk factors to create an enhanced Diabetes diagnostic system which predicts Diabetes in an individual. The performance analysis of this prediction architecture shows that, over other traditional machine learning classifiers, the Optimized Gaussian Naïve Bayes achieves an 81.85% classifier accuracy. This proposed DM prediction system is effective as compared to other diabetes prediction systems found in the literature. According to our experimental study, the OGNB based diabetes mellitus prediction system is more appropriate for DM disease prediction.

Application of bayesian classifier tn diagnosis of lung cancer by multiple autoantibody biomarkers / 第二军医大学学报

Objective To establish a Bayesian classifier-based lung cancer prediction model, and to discuss its predictive efficiency. Methods Using the reaction data of previously screened 6 phage peptide clones with the sera of 90 lung cancer patients and 90 healthy controls, we established a Bayesian classifier-based lung cancer prediction model, with the data analyzed by BinReg 2.0 software. The predictive efficiencies of different models (Bayesian classifier-based prediction model, Logistic regression, principal component regression, and support vector machine) were evaluated by receiver operating characteristic (ROC) curves. Results The sensitivity and specificity of Bayesian classifier-based lung cancer prediction model were 92.00% and 96.00% respectively. And the model satisfactorily distinguished lung cancer patients and healthy controls. Conclusion Our Bayesian classifier-based lung cancer prediction model can accurately predict the risk of lung cancer.

Classification of Human Papillomavirus (HPV) Risk Type via Text Mining

Human Papillomavirus (HPV) infection is known as the main factor for cervical cancer which is a leading cause of cancer deaths in women worldwide. Because there are more than 100 types in HPV, it is critical to discriminate the HPVs related with cervical cancer from those not related with it. In this paper, the risk type of HPVs using their textual explanation. The important issue in this problem is to distinguish false negatives from false positives. That is, we must find high-risk HPVs as many as possible though we may miss some low-risk HPVs. For this purpose, the AdaCost, a cost-sensitive learner is adopted to consider different costs between training examples. The experimental results on the HPV sequence database show that the consideration of costs gives higher performance. The improvement in F-score is higher than that of the accuracy, which implies that the number of high-risk HPVs found is increased.