Chronic Kidney Disease Prediction Using Logistic Regression And Random Forest Model

Chronic Kidney Disease Prediction Using Logistic Regression And Random Forest Model

Registration ID: IJNRD_222348

Published ID: IJNRD2405620

DOI: Click Here to Get

Jaydeep Jadhav , Narayan Kande , Aaditya Kothari , Devendra Surayawanshi , Digvijay Patil

Chronic kidney disease, machine learning, KNN imputation, integrated model

Chronic kidney disease (CKD) represents a significant global health challenge due to its high rates of morbidity and mortality, often leading to additional health complications. The early stages of CKD typically lack noticeable symptoms, resulting in many patients remaining unaware of their condition. Detecting CKD early is crucial, as it allows for timely intervention and can slow disease progression. Machine learning models have shown promise in assisting healthcare professionals with quick and precise disease identification. This study introduces a machine learning approach to CKD diagnosis. The CKD dataset, sourced from the University of California Irvine (UCI) machine learning repository, contains numerous missing values. We employed KNN imputation to address these gaps by selecting several complete samples with the most similar attributes to estimate the missing data for each incomplete sample. In real-world medical settings, missing values are common as patients may skip certain measurements for various reasons. After completing the dataset, we applied six machine learning algorithms: logistic regression, random forest, support vector machine, k-nearest neighbor, naive Bayes classifier, and feedforward neural network were used to establish models. Among these machine learning models, random forest achieved the best performance with 99.75% diagnosis accuracy. By examining the errors produced by the initial models, we developed a combined model that integrates logistic regression and random forest using a perceptron. This integrated model achieved an average accuracy of 99.83% across ten simulations. There

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"Chronic Kidney Disease Prediction Using Logistic Regression And Random Forest Model", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 5, page no.g155-g159, May-2024, Available :https://ijnrd.org/papers/IJNRD2405620.pdf

Volume 9 Issue 5, May-2024

Pages : g155-g159

Paper Reg. ID: IJNRD_222348

Published Paper Id: IJNRD2405620

Downloads: 000121172

Research Area: Information Technology 

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405620.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405620

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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