Machine Learning Models for Prediction and Classification in Chronic Kidney Disease

Aug 3, 2026

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Chronic kidney disease (CKD) is a major worldwide health concern that demands early detection and reliable classification to improve patient outcomes. Using datasets that integrate clinical, demographic, and laboratory information, this study evaluates multiple machine learning models for CKD prediction and categorisation. Decision trees, support vector machines, and neural networks are compared with conventional statistical methods to determine which approach best detects CKD stages and forecasts disease progression, while feature selection enhances efficiency and interpretability.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Need for early detection and accurate CKD stage categorisation

Role of machine learning in medical diagnostics and prognostics

Study scope: model comparison and prediction goals

Feature selection for efficiency and interpretability

Interdisciplinary cooperation for clinical deployment

Machine Learning Models for Prediction and Classification in Chronic Kidney Disease Amit Kumar Bajpai1, Vinay S2, Balakrishna Gudla3*, Khaja Mannanuddin4, Kamalam Ravi5, Rahul Singha6 1Practice Head, Healthcare (Ashconn/Accro), [Email ID: amitkbajpai@outlook.com](Email ID: amitkbajpai@outlook.com) 2Assistant Professor, Department of General Surgery, Raja Rajewari Medical Collage and Hospital, Bengaluru, Karnataka, India, Email ID: [vinchi100@gmail.com](vinchi100@gmail.com), ORCID: 0000-0002-5776-8518 3Associate Professor, Malla Reddy University, Hyderabad, Telangana, India *[Corresponding Author Email ID: gudla.balakrishna@gmail.com](Corresponding Author Email ID: gudla.balakrishna@gmail.com), ORCID: 0000-0001-5658-0233 4Assistant Professor, Department of Computer Science and Engineering, School of CS &AI, SR University, Warangal, India, Email ID: [k.mannanuddin@sru.edu.in](k.mannanuddin@sru.edu.in) 5Assistant Professor of Biochemistry, Sree Balaji Medical College and Hospital, Chromepet, Chennai, India Email ID: [3058kamalam11.apr@gmail.com](3058kamalam11.apr@gmail.com) ORCID: 0000-0002-9625-3058 6PG-Student, Department of Zoology, Pandu College, Assam, India, [Email ID: singharahulzoo23@gmail.com](Email ID: singharahulzoo23@gmail.com) | KEYWORDS | ABSTRACT: | | --- | --- | | Accuracy, Confusion | A major worldwide health concern, chronic kidney disease (CKD) requires early detection and | | Matrix, Feature | efficient classification to enhance patient outcomes. Using a variety of datasets that include clinical, | | Importance, F1-Score, | demographic, and laboratory information, this study explores the use of different machine learning | | False Negatives, False | models designed for the prediction and categorisation of CKD. The main goal of the study is to | | Positives, Machine | compare the effectiveness of sophisticated machine learning approaches, including as decision trees, | | Learning Models, | support vector machines, and neural networks, with conventional statistical methods in order to | | Neural Networks, | ascertain which is better at correctly detecting CKD stages and forecasting the course of the disease. | | Precision, Random | The results show that when compared to traditional techniques, machine learning models perform | | Forest, Recall, RMSE, | better in terms of categorisation and forecast accuracy.Notably, the incorporation of feature selection | | True Positives | approaches improves the efficiency and interpretability of the model, enabling the identification of important risk variables that contribute to chronic kidney disease. This study highlights how machine learning has the potential to revolutionise nephrology by enabling prompt interventions and individualised treatment plans. It also emphasises how crucial interdisciplinary cooperation is to the creation of predictive analytics frameworks that are easily incorporated into clinical practice. The findings point to a paradigm shift in the management of chronic illnesses like CKD towards datadriven healthcare solutions, which would eventually improve patient quality of life and lower healthcare expenses. | INTRODUCTION Millions of people worldwide suffer from chronic kidney disease (CKD), a degenerative illness that places a heavy strain on healthcare systems. If left untreated, chronic kidney disease (CKD), which is characterised by a progressive decline in kidney function, can progress to end-stage renal disease (ESRD) . For prompt interventions, efficient care, and better patient outcomes, early detection and precise CKD stage categorisation are essential. However, the accuracy, scalability, and capacity to offer individualised insights of standard diagnostic techniques are frequently limited. As a result, there is an immediate need for creative solutions to these problems. The field of medical diagnostics and prognostics has seen revolutionary prospects with the introduction of machine learning (ML) . With their capaci

FAQs

Why is early detection and accurate CKD stage categorisation important?

Early detection and precise stage classification enable prompt interventions and better patient outcomes, while standard diagnostic techniques may lack sufficient accuracy and scalability for individualized insights.

Which machine learning models are evaluated for CKD prediction and classification?

The study compares decision trees, support vector machines (SVM), and neural networks, assessing their effectiveness for CKD diagnosis and disease progression prediction.

How does feature selection affect the machine learning models in this study?

Feature selection improves model efficiency and interpretability by reducing computing complexity and helping identify important risk variables associated with chronic kidney disease.

Aug 3, 2026

Upload by Levi