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DOI: 10.14569/IJACSA.2017.080333
PDF

Evaluating Predictive Algorithms using Receiver-Operative Characteristics for Coronary Illness among Diabetic Patients

Author 1: Tahira Mahboob
Author 2: Saman Sahaheen
Author 3: Nuzhat Tahir
Author 4: Mukhtiar Bano

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 3, 2017.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: The grouping of information is a typical method in Machine learning. Information mining assumes a crucial part to extract learning from vast databases from operational databases. In medicinal services Data mining is a creating field of high significance, giving expectations and a more profound comprehension of restorative information sets. Most extreme information mining technique relies on an arrangement of elements that characterizes the conduct of the learning calculation furthermore straightforwardly or by implication impact of the multifaceted nature of models. Coronary illness is the main sources of death over the past years. Numerous scientists utilize a few information digging methods for the diagnosing of coronary illness. Diabetes is one of the incessant maladies that emerge when the pancreas does not deliver enough insulin. The vast majority of the frameworks have effectively utilized Machine learning strategies, for example, Naïve Bayes Algorithm, Decision Trees, logistic Regression and Support Vector Machines to name a few. These techniques solely rely on grouping of the information with respect to finding the heart variations from the norm. Bolster vector machine is an advanced strategy has been effectively in the field of machine learning. Utilizing coronary illness determination, the framework presented predicts using characteristics such as, age, sex, cholesterol, circulatory strain, glucose and the odds of a diabetic patient getting a coronary illness using machine learning algorithms.

Keywords: Artificial neural networks (ANN); Decision tree; Naïve Bayes; Logistic Regression and Clustering

Tahira Mahboob, Saman Sahaheen, Nuzhat Tahir and Mukhtiar Bano, “Evaluating Predictive Algorithms using Receiver-Operative Characteristics for Coronary Illness among Diabetic Patients” International Journal of Advanced Computer Science and Applications(IJACSA), 8(3), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080333

@article{Mahboob2017,
title = {Evaluating Predictive Algorithms using Receiver-Operative Characteristics for Coronary Illness among Diabetic Patients},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080333},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080333},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {3},
author = {Tahira Mahboob and Saman Sahaheen and Nuzhat Tahir and Mukhtiar Bano}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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