An Intelligent Approach for Diabetes Classification, Prediction and Description

A number of machine learning models have been applied to a prediction or classification task of diabetes. These models either tried to categorise patients into insulin and non-insulin, or anticipate the patients’ blood surge rate. Most medical experts have realised that there is a great relationship between patient’s symptoms with some chronic diseases and the blood sugar rate. This paper proposes a diabetes-chronic disease prediction-description model in the form of two sub-modules to verify this relationship. The first sub-module uses Artificial Neural Network (ANN) to classify the types of case and to predict the rate of fasting blood sugar (FBS) of patients. The post-process module is used to figure out the relations between the FBS and symptoms with prediction rate. The second sub-module describes the impact of the rate of FBS and the symptoms on the patient’s health. Decision Trees (DT) is used to achieve the description part of diabetes symptoms.

[1]  Gema García-Sáez,et al.  Automatic Blood Glucose Classification for Gestational Diabetes with Feature Selection: Decision Trees vs. Neural Networks , 2014 .

[2]  B. Yegnanarayana,et al.  Artificial neural networks for pattern recognition , 1994 .

[3]  P. K. Srimani,et al.  Medical Diagnosis Using Ensemble Classifiers - A Novel Machine-Learning Approach , 2013 .

[4]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[5]  Jianping Lu.  A novel feature selection method based on data normalization , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[6]  Nikola K. Kasabov,et al.  On-line pattern analysis by evolving self-organizing maps , 2003, Neurocomputing.

[7]  I N Lee,et al.  Data mining techniques applied to medical information , 2000, Medical informatics and the Internet in medicine.

[8]  Asha Gowda Karegowda,et al.  Rule based Classification for Diabetic Patients using Cascaded K-Means and Decision Tree C4.5 , 2012 .

[9]  Leo Liberti,et al.  Euclidean Distance Geometry and Applications , 2012, SIAM Rev..

[10]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[11]  Erik Berglund,et al.  The parameterless self-organizing map algorithm , 2006, IEEE Transactions on Neural Networks.

[12]  Vaibhav Bajpai,et al.  Application of Artificial Neural Network (ANN) technique for Diagnosing Diabetes Mellitus , 2008, 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems.

[13]  Paola Inverardi,et al.  Model-based performance prediction in software development: a survey , 2004, IEEE Transactions on Software Engineering.

[14]  B. L. Deekshatulu,et al.  Classification of Heart Disease using Artificial Neural Network and Feature Subset Selection , 2013 .

[15]  James F. Baldwin,et al.  Simple Fuzzy Logic Rules Based on Fuzzy Decision Tree for Classification and Prediction Problem , 2004, Intelligent Information Processing.

[16]  Zarita Zainuddin,et al.  A Neural Network Approach in Predicting the Blood Glucose Level for Diabetic Patients , 2009 .

[17]  Qeethara Al-Shayea.  Artificial Neural Networks in Medical Diagnosis , 2024, International Journal of Research Publication and Reviews.

[18]  Ainuddin Wahid Abdul Wahab,et al.  A review on feature selection in mobile malware detection , 2015, Digit. Investig..

[19]  S. Wild,et al.  Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. , 2004, Diabetes care.

[20]  G. Rossi,et al.  Diagnosis and Classification of Diabetes Mellitus The information that follows is based largely on the reports of the Expert Committee on the Diagnosis and Classification of Diabetes (Diabetes Care 20:1183–1197, 1997, and Diabetes Care 26:3160–3167, 2003). , 2008, Diabetes Care.