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ISSN 1004-9037
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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
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      1 Jan 2024, Volume 39 Issue 1   
    Article

    DIABETES DIAGNOSIS USING MACHINE LEARNING AND DATA VISUALIZATION
    Shivam Singh, Nahita Pathania, Novel Biswas, S.Guru Vishnu Vardhan Raju, Dipti Gaurav Mishra
    Journal of Data Acquisition and Processing, 2024, 39 (1): 1397-1407 . 

    Abstract

    The burgeoning prevalence of diabetes globally necessitates swift diagnosis and effective intervention for public health benefit. Leveraging data from the Behavioural Risk Factor Surveillance System (BRFSS) dataset, the current piece of research is trying to figure out if machine learning algorithms can be used for diabetes detection. By analysing a range of health indicators including blood pressure, cholesterol, BMI, lifestyle factors, and socio-demographic features, the research employs algorithms for example Logistic Regression, CatBoost, K-Nearest Neighbours (KNN), Random Forest, Decision Tree to predict diabetes status. Through meticulous data preparation involving dealing with absent data, transforming categorical variables into numerical representations, and standardizing numerical attributes, followed by evaluation using designated test subsets, the study aims to delineate the most efficient algorithmic strategies for diabetes diagnosis, contributing insights to healthcare analytics and proactive disease management.

    Keyword

    Machine Learning, Diabetes diagnosis, Health indicators, Cat Boost, Random Forest, F1-score, Evaluation.


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ISSN 1004-9037

         

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