Machine Learning Hybrid Model for the Prediction of Chronic Kidney Disease

Abstract

To diagnose an illness in healthcare, doctors typically conduct physical exams and review the patient’s medical history, followed by diagnostic tests and procedures to determine the underlying cause of symptoms. Chronic kidney disease (CKD) is currently the leading cause of death, with a rapidly increasing number of patients, resulting in 1.7 million deaths annually. While various diagnostic methods are available, this study utilizes machine learning due to its high accuracy. In this study, we have used the hybrid technique to build our proposed model. In our proposed model, we have used the Pearson correlation for feature selection. In the first step, the best models were selected on the basis of critical literature analysis. In the second step, the combination of these models is used in our proposed hybrid model. Gaussian Naïve Bayes, gradient boosting, and decision tree classifier are used as a base classifier, and the random forest classifier is used as a meta-classifier in the proposed hybrid model. The objective of this study is to evaluate the best machine learning classification techniques and identify the best-used machine learning classifier in terms of accuracy. This provides a solution for overfitting and achieves the highest accuracy. It also highlights some of the challenges that affect the result of better performance. In this study, we critically review the existing available machine learning classification techniques. We evaluate in terms of accuracy, and a comprehensive analytical evaluation of the related work is presented with a tabular system. In implementation, we have used the top four models and built a hybrid model using UCI chronic kidney disease dataset for prediction. Gradient boosting achieves around 99% accuracy, random forest achieves 98%, decision tree classifier achieves 96% accuracy, and our proposed hybrid model performs best getting 100% accuracy on the same dataset. Some of the main machine learning algorithms used to predict the occurrence of CKD are Naïve Bayes, decision tree, K-nearest neighbor, random forest, support vector machine, LDA, GB, and neural network. In this study, we apply GB (gradient boosting), Gaussian Naïve Bayes, and decision tree along with random forest on the same set of features and compare the accuracy score.

Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements

“…Both Khalid et al . [25] and Almansour et al . [24] explore subsets of this dataset by either using only numerical features or examining the performance with a reduced number of features, respectively.…”

“…Presently, CKD diagnoses require laboratory measurements at least three months apart [6], hence, machine learning could reduce wait time for a diagnosis and treatment plan. Both Khalid et al [25] and Almansour et al [24] explore subsets of this dataset by either using only numerical features or examining the performance with a reduced number of features, respectively. However, our study contributes a novel perspective by categorizing features into at-home, monitoring, and laboratory subsets.…”

“…In our study, we leverage a dataset that has been examined in prior works [18,[24][25][26]. The common thread among these studies, like one of our own, is the focus on CKD classification using all available features.…”

Development and validation of a computable phenotype for Turner syndrome utilizing electronic health records from a national pediatric network

“…Finally, we acknowledge that this computable phenotype only accounts for patients who are clinically diagnosed with TS and this diagnosis is documented in the EHR. There are undoubtedly patients with TS who have a delayed and/or missed diagnosis, and future studies could use machine learning approaches to identify EHR variables that predict TS in order to identify patients who do not have a billing diagnosis of TS in their EHR (Alexandrou et al, 2020;Berglund et al, 2019;Khalid et al, 2023).…”

Prognostic Modeling of Chronic Kidney Disease Progression: Bridging Mild and Severe Stages through a Machine Learning Approach

“…In 2013, the toll of Chronic Kidney Disease (CKD) claimed the lives of approximately one million individuals [1]. This burden disproportionately afflicts the developing world, where low to middle-income nations bear the weight of 387.5 million CKD cases, comprising 177.4 million male patients and 210.1 million female patients [2]. These statistics underscore the pervasive nature of CKD within developing regions, and the prevalence continues to surge.Chronic Kidney Disease (CKD) stands as a significant medical issue affecting numerous individuals worldwide.…”