Diagnosis of diabetes using classification mining techniques
By Aiswarya Iyer, J. S, Ronak Sumbaly. arXiv.org, 2015
352 citations recorded by the source corpus.
Diabetes has affected over 246 million people worldwide with a majority of them being women. According to the WHO report, by 2025 this number is expected to rise to over 380 million. The disease has been named the fifth deadliest disease in the United States with no imminent cure in sight. With the rise of information technology and its continued advent into the medical and healthcare sector, the cases of diabetes as well as their symptoms are well documented. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in the data through classification analysis by employing Decision Tree and Na\"ive Bayes algorithms. The research hopes to propose a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.
Subfields: Artificial Intelligence, Health Information Management, Information Systems
View Paper: https://www.semanticscholar.org/paper/ee44d56f3d0c4facf556320a27472c6f9c7f95f6
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Citations in the corpus
- Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms (2022, methodology): “Iyer et al. [34] and Zia and Khan [32] also used a percentage split of 70:30 for the training and testing sets.”
- Survey of Machine Learning Algorithms for Disease Diagnostic (2017, methodology): “[11] has performed a work to predict diabetes disease by using decision tree and Naive Bayes.”