An analysis of computational intelligence techniques for diabetes prediction

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Most of the time early detection and diagnosis of diabetes are very costly and complicated. The main objective of this study is to evaluate the performance of different Machine Learning algorithms in order to reduce the cost of the treatment. Considering diabetes, early prediction of diabetes is an important issue in Health Care Services (HCS). So, there is a need for an application that can effectively diagnosis thousands of patients using medical specifications. In this work, we examine different machine learning algorithms for predicting diabetes in real time by drawing from ideas and techniques in the field of machine learning. This study used 4 classification techniques for diabetes prediction. Such as, Artificial Neural Network (ANN), Random Forest (RF), Naive Bayes (NB) and Support Vector Machine (SVM). The performance of different classification techniques was evaluated on different measurement techniques. Moreover, the present study mainly focusses on the use of medical code data for disease prediction and explore different ways for representing such data in my prediction algorithms.

     

     


  • Keywords


    Machine Learning; Classification; Disease Prediction; Diabetes.

  • References


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Article ID: 28245
 
DOI: 10.14419/ijet.v7i4.28245




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