A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Diabetes

Data mining techniques are applied in many applications as a standard procedure for analyzing the large volume of available data, extracting useful information and knowledge to support the major decision-making processes. Diabetes mellitus is a continuing, general, deadly syndrome occurring all around the world. It is characterized by hyperglycemia occurring due to abnormalities in insulin secretion which would in turn result in irregular rise of glucose level. In recent years, the impact of Diabetes mellitus has increased to a great extent especially in developing countries like India. This is mainly due to the irregularities in the food habits and life style. Thus, early diagnosis and classification of this deadly disease has become an active area of research in the last decade. Numerous clustering and classifications techniques are available in the literature to visualize temporal data to identify trends for controlling diabetes mellitus. This work presents an experimental study of several algorithms which classifies Diabetes Mellitus data effectively. The existing algorithms are analyzed thoroughly to identify their advantages and limitations. The performance assessment of the existing algorithms is carried out to determine the best approach.

[1]  B Harini,et al.  An efficient feature selection method for classification in health care systems using machine learning techniques , 2011, 2011 3rd International Conference on Electronics Computer Technology.

[2]  Anupam Shukla,et al.  Diagnosis of breast cancer by modular evolutionary neural networks , 2011 .

[3]  Health Intervention Impact Assessment on Glycemic Status of Diabetic Patients , 2013 .

[4]  Chee Peng Lim,et al.  A hybrid intelligent system for medical data classification , 2014, Expert Syst. Appl..

[5]  P. Thangaraj,et al.  Clustering and Classifying Diabetic Data Sets Using K-means Algorithm , 2013 .

[6]  Deok Won Kim,et al.  Screening for Prediabetes Using Machine Learning Models , 2014, Comput. Math. Methods Medicine.

[7]  Chung-Ho Hsieh,et al.  Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks. , 2011, Surgery.

[8]  T. Karthikeyan,et al.  An Intelligent Type-II Diabetes Mellitus Diagnosis Approach using Improved FP-growth with Hybrid Classifier Based Arm , 2015 .

[9]  T. Santhanam,et al.  Application of K-Means and Genetic Algorithms for Dimension Reduction by Integrating SVM for Diabetes Diagnosis , 2015 .

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

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

[12]  E. Gaillard,et al.  Early diagnosis of Diabetes mellitus through the eye , 2014 .

[13]  S. Margret Anouncia,et al.  Design of a Diabetic Diagnosis System Using Rough Sets , 2013 .

[14]  M. S. Klein,et al.  Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application , 2015, Journal of diabetes research.

[15]  Krishnamoorthi Makkithaya,et al.  Learning from a Class Imbalanced Public Health Dataset: a Cost-based Comparison of Classifier Performance , 2017 .

[16]  Mahmudur Rahman,et al.  Detection of the Onset of Diabetes Mellitus by Bayesian Classifier Based Medical Expert System , 2016 .

[17]  Fevzullah Temurtas,et al.  A comparative study on diabetes disease diagnosis using neural networks , 2009, Expert Syst. Appl..

[18]  Rashedur M. Rahman,et al.  Comparison of Various Classification Techniques Using Different Data Mining Tools for Diabetes Diagnosis , 2013 .

[19]  M. Kannan,et al.  Analysis of a Population of Diabetic Patients Databases in Weka Tool , 2011 .

[20]  Tole Sutikno,et al.  Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases , 2015 .

[21]  Subramanian Appavu,et al.  An amalgam KNN to predict diabetes mellitus , 2013, 2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN).

[22]  Minal B. Wankhade.  Analysis of Disease using Retinal Blood Vessels Detection , 2016 .

[23]  Allam Appa Rao,et al.  A computational intelligence approach for a better diagnosis of diabetic patients , 2014, Comput. Electr. Eng..

[24]  Kavitha Rani Balmuri,et al.  Hybrid Approach for Prediction of Cardiovascular Disease Using Class Association Rules and MLP , 2016 .