论文信息 - Detection of Diabetes Using Multilayer Perceptron

Detection of Diabetes Using Multilayer Perceptron

Diabetes is a common disease throughout the world. It is generally in sleep mode, whenever the patient suffers from any disease, diabetes boosts them. It is a common factor of cardiac problem. Authors try to detect it using multilayer perceptron neural network in this paper. The case study is of Indian ladies with pregnancy suffer from diabetes. Data considered from PIMA database from UCI repository and are used. Eight attributes are taken as features for each subject. It has been verified for 768 patients. The common MLP classifier is utilized for attributes and the experiment is learned with R studio platform. The performance found to be better as compared to earlier methods and verified in MATLAB platform as well.

Mihir Narayan Mohanty | Saumendra Kumar Mohapatra | Jagjit Kumar Swain

[1] Jude W. Shavlik,et al. Using neural networks for data mining , 1997, Future Gener. Comput. Syst..

[2] Mahesh Chandra,et al. Use of Different Features for Emotion Recognition Using MLP Network , 2015 .

[3] D. R. Patil,et al. Prediction of heart disease using multilayer perceptron neural network , 2014, International Conference on Information Communication and Embedded Systems (ICICES2014).

[4] Ayman El-Baz,et al. Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm , 2017, Comput. Methods Programs Biomed..

[5] Karim Keshavjee,et al. Performance Analysis of Data Mining Classification Techniques to Predict Diabetes , 2016 .

[6] Mihir Narayan Mohanty,et al. Design of MLP Based Model for Analysis of Patient Suffering from Influenza , 2016 .

[7] Guang-Bin Huang,et al. Extreme Learning Machine for Multilayer Perceptron , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[8] Andrew P. Bradley,et al. Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus , 2010, IEEE Transactions on Information Technology in Biomedicine.

[9] Mehrbakhsh Nilashi,et al. Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical Dataset , 2017 .