Evaluating the Efficacy of Machine Learning Compared to Traditional Methods in Kidney Calcification Diagnosis: A Comprehensive Review

Abstract

Phosphorus dysregulation is a major factor in kidney calcification, a serious consequence of chronic kidney disease (CKD) caused by mineral imbalance. Effective treatment of kidney calcification is needed to improve patient outcomes in chronic kidney disease. This study compares the efficiency, accuracy, flexibility and capability of conventional and machine learning (ML) based methods for appropriate care in managing this disease. To manage mineral levels and prevent calcification, conventional treatments such as dietary modifications, phosphate binders, dialysis, and phosphorus regulating drugs are still critical. Emerging machine learning techniques such as data-driven therapeutic optimization and predictive modeling provide a possible alternative to these traditional approaches. ML can identify high-risk individuals and deliver customized therapies, predict disease courses, enable early treatment, and improve long-term calcification management by using sophisticated algorithms and large datasets. This study hypothesizes that compared to traditional methods alone, ML-driven approaches can improve treatment outcomes by providing more accurate, data-informed and patient-specific care. In addition, the ability of ML to match the unique characteristics of each patient may reduce problems and improve the quality of life of CKD patients. The results of this study aim to improve traditional approaches and encourage proactive patient care by guiding the use of machine learning as a complementary tool in the management of chronic kidney disease.