Ordered logistic regression with artificial neural network models for variable selection for prediction of hypertension patient outcomes - Mohamad Ghazali, Farah Muna; Wan Ahmad, Wan Muhamad Amir; Awang Nawi, Mohamad Arif; Mohd Noor, Nor Farid; Ghazalli, Nur Fatiha; Aleng, NorAzlida; Mohd Ibrahim, Mohamad Shafiq; Abdul Halim, Nurfadhlina (Sapporo Ika Daigaku, 2020)
The purpose of this study is to demonstrate the best strategy for the variable selection, using the developed Ordered Logistic Regression (OLR) and Multilayer Perceptron Neural Network (MLP). At the first stage, all the selected variables will be a screen for their important relationship point of view through ordered logistics regression and bootstrap methodology. After considering for 1500 of the bootstrapping methods, it was found that smoking factor, total cholesterol factor, and triglycerides come to a significant relationship to the level of hypertension. By considering the level of significance of 0.25 for ordered logistic regression, these three variables are being selected and used for the input of the MLP model. The performance of MLP was evaluated through the Predicted Mean Square Error (PMSE) of the neural network for the (MSE-forecasts the Network). PMSE is used as a measurement of how far away from our predictions are from the real data. The smallest MSE from MLP, indicate the best combination of variables selection in the model. In this research paper, we also provide the R syntax for OLR and MLP better illustration.