• Chayan Bhattacharjee

This review examines the paper "Analysis of the Performance of Feature Optimisation Techniques for the Diagnosis of Machine Learning-Based Chronic Kidney Disease." The research looks into using machine learning to improve the accuracy of chronic kidney disease (CKD) diagnosis. The study uses the Cleveland Kidney Disease dataset to embark on a comprehensive journey that includes data pre-processing, feature selection, model application, and evaluation. The Minimal Redundancy-Maximal-Relevance (MRMR) algorithm is an important tool for feature reduction, which results in a refined dataset. For CKD diagnosis, machine learning models such as linear regression, support vector machine (SVM), decision tree, k-nearest neighbours (KNN), and Linear Discriminant Analysis (LDA) are used. To assess model effectiveness, rigorous performance evaluation metrics are used. LDA stands out as the best performer, with a 99.5% accuracy rate. Further evaluations will involve comparing the new LDA-based model to existing ones and demonstrating accuracy improvements. The review emphasises the importance of feature optimisation and appropriate model selection in improving CKD diagnosis accuracy, paving the way for improved patient care and early intervention.

Machine learning, CKD, diagnosis, Feature optimization

"Analysis of performance of feature optimization techniques for the diagnosis of Chronic Kidney Disease using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.c502-c505, June-2024, Available :http://www.jetir.org/papers/JETIR2406264.pdf

"Analysis of performance of feature optimization techniques for the diagnosis of Chronic Kidney Disease using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppc502-c505, June-2024, Available at : http://www.jetir.org/papers/JETIR2406264.pdf