FORESTALLING GROWTH RATE IN TYPE II DIABETIC PATIENTS USING DATA MINING AND ARTIFICIAL NEURAL NETWORKS: AN INTENSE SURVEY

Abstract

The race for urbanization and thirst for high living status leads to unhealthy life. As the result a rapid growth in number of diabetic patients in urban areas approaching to its deadline. In this situation it become a prime necessity for physicians and health workers to recognize accurate growth rate in number of diabetic patients. Artificial Neural Network is used as one of the artificial intelligent technique for forestalling growth rate of type II diabetic patients. Diabetes occurred due to increased level of glucose in blood. In this paper, an intense survey is done for the prediction of Type II diabetes using different Data Mining tools and Artificial Neural Network techniques, is presented. This survey is aimed to recognize and propose an effective technique for earlier prediction of the Type II diabetes. The data mining techniques like C4.5 Classifier, Support Vector Machine and K-Nearest Neighbour are compared for this work with Artificial Neural Network. As the results Artificial Neural Network found with a great accuracy of 89%.

Authors and Affiliations

KIRAN BALA DUBEY and GYANESH SHRIVASTAVA

Keywords

Related Articles

PREDICTING SAFETY INFORMATION OF DRUGS USING DATA MINING TECHNIQUE

Data Classification is the application of data mining techniques to discover patterns from the micro array and biological datasets. This research entitled “PREDICTING SAFTEY INFORMATION OF DRUGS USING DATA MINING TECHN...

FAULT DATA DETECTION IN SOFTWARE USING A NOVEL FGRNN ALGORITHM

The use and dependence on software in various fields has been the reason why researchers for past decades have spent their efforts on finding better methods to predict software quality and reliability. Soft computing m...

MULTIPLE KERNEL FUZZY CLUSTERING FOR UNCERTAIN DATA CLASSIFICATION

Traditional1call tree classifiers work1with information whose values1area unitcelebrated and precise. We1have a tendency to extend1such classifiers to handle information1with unsureinfo. Worth uncertainty1arises in sev...

AN OPTIMAL COMPOSITION PLAN SELECTION USING MULTI OBJECTIVE PARTICLE SWARM OPTIMIZATION

Domain-ontology based Particle Swarm Optimization (PSO)-inspired Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) and Improved Bipartite graph is an efficient web service composition approach. It co...

UNDERSTANDING ADOPTION FACTORS OF OVER-THE-TOP VIDEO SERVICES AMONG MILLENNIAL CONSUMERS

With growing digitization, the challenge for marketers is to understand how consumers consuming Over-The –Top (OTT) content adopt and consume messages in this format effectively. Superimposing the theoretical framework...

Download PDF file
  • EP ID EP46556
  • DOI 10.34218/IJCET.10.3.2019.004
  • Views 189
  • Downloads 0

How To Cite

KIRAN BALA DUBEY and GYANESH SHRIVASTAVA (2019). FORESTALLING GROWTH RATE IN TYPE II DIABETIC PATIENTS USING DATA MINING AND ARTIFICIAL NEURAL NETWORKS: AN INTENSE SURVEY. International Journal of Computer Engineering & Technology (IJCET), 10(3), -. https://europub.co.uk./articles/-A-46556