Comparing Machine Learning Techniques for Detecting Chronic Kidney Disease in Early Stage

CKD is a gradual disease that affects millions of people throughout the United States and results in high morbidity and mortality rates. Chronic Kidney Disease is an ailment that culminates in a gradual loss of kidney function over t,ime. Early detection is essential since timely interventions may prevent the progression of CKD, improve outcomes and survival for patients with CKD, and reduce healthcare costs. In the recent decade, machine learning models have emerged as a game-changing tool in medical diagnostics, leveraging big data and complex algorithms to find patterns almost invisible to clinicians and physicians. This study deployed and evaluated various machine learning approaches for the early detection of CKD, focusing on their comparative performance, strengths, and weaknesses. Machine learning transforms medical diagnosis by leveraging big data and sophisticated algorithms to find patterns that might otherwise elude healthcare professionals. The dataset used for this research will be the CKD dataset, which was contributed to by the Cleveland Clinic in 2021. The dataset can be accessed publicly through the University of California, Irvine's UCI Machine Learning Repository. In this project, the analyst compared and contrasted the performance of Logistic Regression, Decision Trees, and Random Forests. Experimentation results demonstrated that logistic regression had the best performance, yielding a perfect F1 score and accuracy, closely followed by random forest. This result showed that the Logistic model ideally classified all the instances in the test set. Consolidating machine learning algorithms into the early detection of Chronic Kidney Disease (CKD) holds substantial promise for transforming clinical practice. Healthcare professionals can enhance diagnostic accuracy and facilitate timely interventions by leveraging proposed algorithms such as logistic regression.