Machine Learning-Based Prognostication of Chronic Kidney Disease

Abstract

Chronic kidney disease (CKD) is a potentially life-long condition characterized by either renal malignancy or reduced renal function. It is possible to impede or decelerate the advancement of this chronic ailment to a terminal state, necessitating dialysis or surgical intervention as the sole means of sustaining a patient’s life. Identifying potential issues earlier and implementing suitable therapeutic interventions can enhance the probability of this occurrence. This study investigates the possibility of different machine-learning algorithms for early chronic kidney disease (CKD) detection. There has been a notable surge in the utilization of social media platforms among individuals in recent years. Extensive research has been performed on this specific subject. However, we are improving the methodology we employ through implementation. The application of predictive modeling techniques. Therefore, our methodology involves an examination of the correlation that exists across different data components. The traits of the designated category have been identified. With support, we can construct a comprehensive set of forecasting models. Machine learning and anticipatory analytics have experienced advancements due to improved methods of incorporating attributes. Predictive modeling is a statistical technique for making predictions or forecasts based on historical data and patterns. It involves the development and application of this study commences with a total of 25 variables along with the class attribute. However, as the investigation progresses, the number of variables gradually diminishes until it concludes. The list has been reduced to 30 percent of the criteria, the most suitable subset for identifying chronic kidney disease (CKD). There are three distinct machines. Development-based classifiers have been subjected to evaluation within a framework of supervised learning. In a rigorously supervised setting, a thorough examination has been undertaken of three discrete classifiers that rely on machine learning methodologies within the educational environment. The performance measures of utmost importance exhibit an accuracy level of 0.99, a precision value of 1, a recall rate of 0.98, and an F1-score of 0.99. The decision tree classifier learning algorithms have significantly contributed to the advancements in artificial intelligence. Using predictive modeling in the learning process presents a captivating approach to discovering novel solutions, enhancing the endeavor’s overall efficacy. This study assesses prognostication’s precision in renal disorders and other related domains.
Keywords: Chronic Kidney Disease (CKD), Machine Learning, Early Prediction, Modeling, KNN, Decision Tree