CHAPTER – II LITERATURE REVIEW ON DATA MINING METHODS IN MEDICINE (original) (raw)

Diabetes Prediction: A Study of Various Classification based Data Mining Techniques

Arup Mohanty

International Journal of Computer Science and Informatics, 2022

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Prediction of Diabetes using Classification Algorithms

Dr. Deepti Sisodia

Procedia Computer Science, 2018

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Effectiveness of Classification Methods on the Diabetes System

dr Ismail Hagag

Asian Journal of Research in Computer Science, 2021

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Classification of Diabetes patient by using Data Mining Techniques

Latika Kakkar, Dr Mukesh Kumar

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An Analysisof Data Mining Classification Methods for Medical Dataset

Angeline Christobel

Journal of emerging technologies and innovative research, 2019

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Performance Analysis of Data Mining Classification Algorithm to Predict Diabetes

E. Karthikeyan

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Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran

Jay Vala

International Journal of Diabetes in Developing Countries, 2015

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A Review on Diabetes Mellitus diagnoses using classification on Pima Indian Diabetes Data Set

International Journal of Advance Research in Computer Science and Management Studies [IJARCSMS] ijarcsms.com

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Analysis Of A Population Of Diabetic Patients Databases With Classifiers

Murat KOKLU

2013

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Comparative Study and Analysis of Classification Algorithms In Data Mining Using Diabetic Dataset

International Journal of Scientific Research in Science and Technology IJSRST

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A Diabetic Disease Prediction Model Based on Classification Algorithms

Annals of Emerging Technologies in Computing (AETiC)

Annals of Emerging Technologies in Computing (AETiC), 2019

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Prediction of Diabetes using a Classification model

Beulah Christalin Latha 1885

2017

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An Empirical Comparison by Data Mining Classification Techniques for Diabetes Data Set

Nilesh Vispute

2015

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Prediction of Diabetes Disease using Data Mining Classification Techniques

Dawood Saddique

2019

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Classification of Diabetes Patient Using Machine Learning Approaches

Nada Tade

2017

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Comparison of Various Classification Techniques Using Different Data Mining Tools for Diabetes Diagnosis

M. Rashedur Rahman

Journal of Software Engineering and Applications, 2013

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On The Analysis of Some Machine Learning Algorithms for the Prediction of Diabetes

Bello A Buhari

International Journal of Advanced Networking and Applications

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Diabetes Prediction System

IJRASET Publication

International Journal for Research in Applied Science and Engineering Technology (IJRASET), 2022

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Data Mining Algorithms Application in Diabetes Diseases Diagnosis: A Case Study

Masoum Farahmandian

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A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Diabetes

rat pat, International Journal of Electrical and Computer Engineering (IJECE)

International Journal of Electrical and Computer Engineering (IJECE), 2018

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Indepth Analysis of Medical Dataset Mining: A Comparitive Analysis on a Diabetes Dataset Before and After Preprocessing

Ammar Al-Dallal

KnE Social Sciences, 2019

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Application of Data mining methods in the Diagnosis of UCI based Pima Indians Diabetes Database

manish rai

2019

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Prediction Model for Diabetes Mellitus Using Machine Learning Techniques

NAFEES FAROOQUI, A. Tyagi

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Mining Significant Features of Diabetes through Employing Various Classification Methods

Abu Tareq Rony

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A Data Mining Approach for Prediction and Treatment Ofdiabetes Disease

Lakshmi Velide

2014

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Analysis of Data for Diabetics Patient

European Scientific Journal ESJ

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Diabetic Prediction System Using Data Mining

Sanjaya De Silva, Kasun Jinasena

Proceedings in Computing, 9th International Research Conference-KDU, 2016

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Prediction of Female Diabetic Patient in India Using Different Learning Algorithms

Samir Bandyopadhyay

2021

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Diagnosis of Type-2 Diabetes using Classification and Mining Techniques

SANKAR P

International Journal of Engineering and Advanced Technology, 2020

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Diabetes Mellitus Prediction using Classification Techniques

Salum Hassan

International Journal of Innovative Technology and Exploring Engineering, 2020

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