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Adoption of Machine Learning and Data Mining tools in the identification and prediction of Diabetes Disease in Patients Using Classification Mining Algorithm
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
The aim of this paper is to adopt Machine Learning and Data Mining tools in predicting if patient diabetes is positive or negative using classification mining algorithm. The objective of the research includes to analyze a dataset using classification mining tool to predict or identify if a patient has diabetes or not, to use the analyzed data result to improve the health standard of diabetic patients suffering from the disease, to recommend the perfect data mining technique best for analyzing and predicting data. The research was motivated due to the high increase of diabetic patient witnessed in Nigerian Hospitals because of the high intake of Carbohydrate based on report issued by world health organization (WHO) on a yearly basis. Data mining methodology called classification algorithm was adopted while decision tree was used as the modeling tool. The data was analyzed with R and SAS Enterprise Miner. The experiments are done on Pima Indians Diabetes Database (PIDD) sourced from UCI machine learning repository. The result after the experiment shows that the use of classification algorithm and decision model is the best and more accurate method suitable for data prediction and hence has more percentage acceptance level of performance when it comes to health issues, therefore it could be adopted for future use by medical practitioners to make decision on the subject matter.
Keywords
Artificial Intelligence, Machine Learning, Diabetics, Classification Model and Data Mining
References
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How to cite this paper
@article{1704102,
author = {Ibekwe Abundance Emerie, Oguoma Ikechukwu Stanley, Obialor Collins Chimezie, Obichere Chigozie Daniel, Njoku Tochukwu Stanley; Magnus Chinonso Okere},
title = {Adoption of Machine Learning and Data Mining tools in the identification and prediction of Diabetes Disease in Patients Using Classification Mining Algorithm},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {8},
pages = {195-204},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/17041022.pdf},
abstract = {The aim of this paper is to adopt Machine Learning and Data Mining tools in predicting if patient diabetes is positive or negative using classification mining algorithm. The objective of the research includes to analyze a dataset using classification mining tool to predict or identify if a patient has diabetes or not, to use the analyzed data result to improve the health standard of diabetic patients suffering from the disease, to recommend the perfect data mining technique best for analyzing and predicting data. The research was motivated due to the high increase of diabetic patient witnessed in Nigerian Hospitals because of the high intake of Carbohydrate based on report issued by world health organization (WHO) on a yearly basis. Data mining methodology called classification algorithm was adopted while decision tree was used as the modeling tool. The data was analyzed with R and SAS Enterprise Miner. The experiments are done on Pima Indians Diabetes Database (PIDD) sourced from UCI machine learning repository. The result after the experiment shows that the use of classification algorithm and decision model is the best and more accurate method suitable for data prediction and hence has more percentage acceptance level of performance when it comes to health issues, therefore it could be adopted for future use by medical practitioners to make decision on the subject matter.},
keywords = {Artificial Intelligence, Machine Learning, Diabetics, Classification Model and Data Mining},
month = {February},
}