UGC Approved Journal no 63975(19)

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Published in:

Volume 9 Issue 12
December-2022
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2212522


Registration ID:
505303

Page Number

f160-f162

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Title

AN EXPERIMENTAL STUDY ON MACHINE LEARNING TECHNIQUES FOR DIABETIC DISEASE PREDICTION

Abstract

Diabetics is a considered as complex and wide spread disease as it is rapidly evolving with many peoples. Currently, duration and cost of the treatment process is long and very high due to its high recurrence. Diabetes is a group of metabolic diseases characterized by hyperglycaemia resulting from defects in insulin secretion and insulin action. The chronic hyperglycaemia of diabetes is associated with long-term damage, dysfunction, and failure of different organs, especially the eyes, kidneys, nerves, heart, and blood vessels Accurate early diagnosis prediction of diabetics are become more essential to enhance the patient’s treatment procedure. Using machine learning techniques early detection of the disease are made possible. Enabling automated detection and classification of the disease can be carried out using machine learning techniques with low cost and early diagnosis of the disease. In this paper, experimental study on machine learning technique for diabetic’s classification has been carried on basis of defining the disease, diagnosis of the disease, classification of the disease on basis of feature processing. Machine learning model is capable of learning the features of the disease extracted from feature extraction and feature selection model. Classification of the patterns has been represented into types. Classification results are highly discriminant with enhanced classification rate on the dynamic characteristics of the dataset. Evaluation of the technique is estimated using PIMA datasets. The evaluation of the classification technique has been done in accordance with the feature extraction and feature selection methods. Finally the performance analysis of the technique has performed with respect to classification accuracy and execution time to attain the effective results on the cross fold validation of the dataset using confusion matrix on basis of precision, recall and f measures

Key Words

Machine Learning, Diabetics, Classification, Diabetic Disease Types, Feature Selection. Feature Extraction

Cite This Article

"AN EXPERIMENTAL STUDY ON MACHINE LEARNING TECHNIQUES FOR DIABETIC DISEASE PREDICTION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 12, page no.f160-f162, December-2022, Available :http://www.jetir.org/papers/JETIR2212522.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"AN EXPERIMENTAL STUDY ON MACHINE LEARNING TECHNIQUES FOR DIABETIC DISEASE PREDICTION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 12, page no. ppf160-f162, December-2022, Available at : http://www.jetir.org/papers/JETIR2212522.pdf

Publication Details

Published Paper ID: JETIR2212522
Registration ID: 505303
Published In: Volume 9 | Issue 12 | Year December-2022
DOI (Digital Object Identifier):
Page No: f160-f162
Country: Coimbatore, Tamilnadu, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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