Chronic Renal Disease Multi-Classification Using an Intelligent Hybrid Machine Learning Model
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Abstract
Abstract:
Introduction: Chronic Renal Disease (CRD) is a progressive and life-threatening medical condition that significantly affects global public health. Early and accurate classification of chronic renal illness is essential to prevent complications and improve patient survival rates. Traditional diagnostic approaches often face limitations in handling high-dimensional and complex clinical data. To address these challenges, this study proposes an intelligent hybrid machine learning model that integrates multiple algorithms to enhance prediction accuracy and robustness. The proposed framework aims to support clinicians with reliable, data-driven decision assistance for efficient CRD classification and management.
Objectives: The primary objective of this study is to develop an intelligent hybrid machine learning model for accurate classification of Chronic Renal Illness Disorder. It aims to integrate multiple machine learning algorithms to enhance prediction performance and improve classification robustness.The study seeks to analyze clinical and laboratory parameters to identify significant features contributing to early diagnosis.Another objective is to reduce misclassification rates by optimizing model parameters and applying effective feature selection techniques.
Methods: The study begins with comprehensive data preprocessing, including handling missing values, normalization, and feature selection to improve data quality. Exploratory data analysis is performed to identify significant clinical attributes associated with Chronic Renal Illness Disorder. Multiple machine learning algorithms such as Support Vector Machine (SVM), Random Forest, and Logistic Regression are trained individually. An intelligent hybrid model is then developed by integrating the strengths of selected classifiers using ensemble or voting techniques.
Results: The proposed intelligent hybrid machine learning model achieved superior classification performance compared to individual base classifiers. The hybrid approach demonstrated higher accuracy, precision, recall, and F1-score in detecting Chronic Renal Illness Disorder. Feature selection significantly improved model efficiency by reducing dimensionality while maintaining predictive strength. Cross-validation results confirmed the robustness and generalization capability of the proposed framework.
Conclusions: The proposed intelligent hybrid machine learning model effectively enhances the classification of Chronic Renal Illness Disorder with improved accuracy and reliability. By combining multiple algorithms, the hybrid approach overcomes the limitations of individual classifiers and ensures robust predictive performance. The integration of feature selection and optimization techniques further strengthens model efficiency and generalization capability.
The results demonstrate the potential of data-driven methodologies in supporting early detection and timely clinical intervention.
Article Details
References
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