Early Prediction of Diabetes Disease & Classification of Algorithms Using Machine Learning Approach

Diabetes is a global health issues that usually prolonged in a patient for an entire life. Diabetes effect people of all age groups. Development of technology is a non-conventional approach to predict diabetes and provide accurate results with better efficiency. Most of the researches have been carried out to predict the diabetes in which most of the researchers have used Pima Indian dataset. In this paper, authors build a Framework that can estimate with maximum precision, probability of diabetes in Patients. In this paper, authors build a Framework that can estimate with maximum precision, probability of diabetes in Patients. As a result, authors would like to use Machine Learning approach such as Decision Trees, SVM and Naive Bayes to detect diabetes in early phase. The Pima Indian Diabetes that are obtained from UCI library test to measure although these algorithms are used to predict diabetes in order to save time and get accurate results.

[1]  Nikita Joshi.  Improving Classification Accuracy Using Ensemble Learning Technique (Using Different Decision Trees) , 2014 .

[2]  L. Vinet,et al.  A ‘missing’ family of classical orthogonal polynomials , 2010, 1011.1669.

[3]  Jianchao Han,et al.  Discovering Decision Tree Based Diabetes Prediction Model , 2008 .

[4]  Rajani K. Mudi,et al.  Particle Swarm Optimization Based Adaptive PID Controller for pH-Neutralization Process , 2014, FICTA.

[5]  Harleen Kaur,et al.  Predictive modelling and analytics for diabetes using a machine learning approach , 2020, Applied Computing and Informatics.

[6]  Thomas J. Watson,et al.  An empirical study of the naive Bayes classifier , 2001 .

[7]  Ayush Singhal,et al.  MobDBTest: A machine learning based system for predicting diabetes risk using mobile devices , 2015, 2015 IEEE International Advance Computing Conference (IACC).

[8]  S. Balamurali,et al.  Performance Analysis of Classifier Models to Predict Diabetes Mellitus , 2015 .

[9]  Daniele Ramazzotti,et al.  Data Pre-processing , 2016 .

[10]  B. Dhomse Kanchan,et al.  Study of machine learning algorithms for special disease prediction using principal of component analysis , 2016, 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC).

[11]  Alaa F. Sheta,et al.  Developing a Mathematical Model to Detect Diabetes Using Multigene Genetic Programming , 2014 .

[12]  V. Veena Vijayan,et al.  Prediction and diagnosis of diabetes mellitus — A machine learning approach , 2015, 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS).

[13]  Paul Varghese,et al.  Robust Optimized Artificial Neural Network Based PEM Fuelcell Voltage Tracking , 2015, IBICA.

[14]  Nongyao Nai-arun,et al.  Comparison of Classifiers for the Risk of Diabetes Prediction , 2015 .

[15]  Iqbal H. Sarker,et al.  Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus , 2019, 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE).

[16]  Gaurav Tripathi,et al.  Early Prediction of Diabetes Mellitus Using Machine Learning , 2020, 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO).

[17]  T. Yıldırım,et al.  MEDICAL DIAGNOSIS ON PIMA INDIAN DIABETES USING GENERAL REGRESSION NEURAL NETWORKS , 2003 .

[18]  Maruf Pasha,et al.  Survey of Machine Learning Algorithms for Disease Diagnostic , 2017 .

[19]  Dilip Singh Sisodia,et al.  Prediction of Diabetes using Classification Algorithms , 2018 .

[20]  I. Vlahavas,et al.  Machine Learning and Data Mining Methods in Diabetes Research , 2017, Computational and structural biotechnology journal.

[21]  Sudhir N. Dhage,et al.  Diabetes Disease Prediction Using Machine Learning on Big Data of Healthcare , 2018, 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA).

[22]  R. Subashini,et al.  Prevalence of diabetes and prediabetes (impaired fasting glucose and/or impaired glucose tolerance) in urban and rural India: Phase I results of the Indian Council of Medical Research–INdia DIABetes (ICMR–INDIAB) study , 2011, Diabetologia.

[23]  Stephen R. Garner,et al.  WEKA: The Waikato Environment for Knowledge Analysis , 1996 .

[24]  S. Jeyalatha,et al.  Diagnosis of diabetes using classification mining techniques , 2015, ArXiv.

[25]  Munam Ali Shah,et al.  Prediction of Diabetes Using Machine Learning Algorithms in Healthcare , 2018, 2018 24th International Conference on Automation and Computing (ICAC).

[26]  Samrat Kumar Dey,et al.  Implementation of a Web Application to Predict Diabetes Disease: An Approach Using Machine Learning Algorithm , 2018, 2018 21st International Conference of Computer and Information Technology (ICCIT).

[27]  Suman,et al.  Comparative Analysis of Classification Algorithms on Different Datasets using WEKA , 2012 .

[28]  Karim M. Orabi,et al.  Early Predictive System for Diabetes Mellitus Disease , 2016, ICDM.

[29]  Donato Malerba,et al.  A Comparative Analysis of Methods for Pruning Decision Trees , 1997, IEEE Trans. Pattern Anal. Mach. Intell..