Abstract
The incidence, prevalence, and progression of chronic kidney disease (CKD) conditions have evolved over time, especially in countries that have varied social determinants of health. In most countries, diabetics and hypertension are the main causes of CKDs. The global guidelines classify CKD as a condition that results in decreased kidney function over time, as indicated by glomerular filtration rate (GFR) and markers of kidney damage. People with CKDs are likely to die at an early age. It is crucial for doctors to diagnose various conditions associated with CKD in an early stage because early detection may prevent or even reverse kidney damage. Early detection can provide better treatment and proper care to the patients. In many regional hospital/clinics, there is a shortage of nephrologists or general medical persons who diagnose the symptoms. This has resulted in patients waiting longer to get a diagnosis. Therefore, this research believes developing an intelligent system to classify a patient into classes of ‘CKD’ or ‘Non-CKD’ can help the doctors to deal with multiple patients and provide diagnosis faster. In time, organizations can implement the proposed machine learning framework in regional clinics that have lower medical expert retention, this can provide early diagnosis to patients in regional areas. Although, several researchers have tried to address the situation by developing intelligent systems using supervised machine learning methods, till date limited studies have used unsupervised machine learning algorithms. The primary aim of this research is to implement and compare the performance of various unsupervised algorithms and identify best possible combinations that can provide better accuracy and detection rate. This research has implemented five unsupervised algorithms, K-Means Clustering, DB-Scan, I-Forest, and Autoencoder. And integrating them with various feature selection methods. Integrating feature reduction methods with K-Means Clustering algorithm has achieved an overall accuracy of 99% in classifying the clinical data of CKD and Non-CKD.
Highlights
Chronic Kidney Disease (CKD) indicates a condition where human kidneys that are damaged [1] and unable to filter the blood stream and get rid of the metabolic waste the way they are supposed to
Our research aims to ascertain whether Chronic Kidney Disease is present at an early stage by deploying various unsupervised algorithms on patients' data and validating the classifications to ensure their accuracy
Validation scores obtained by considering all the 24 features for DB scan, K-means, I-forest, and Autoencoder are given in the table 10
Summary
Chronic Kidney Disease (CKD) indicates a condition where human kidneys that are damaged [1] and unable to filter the blood stream and get rid of the metabolic waste the way they are supposed to. CKD usually develops gradually over a significant amount of time. More than 800 million people all over the world [2] are found to be affected by kidney disease including the CKD. Identifying someone as having CKD requires two sets of samples, taken at least 90 days apart [8]. The estimated Glomerular Filtration Rate (eGFR) depends on creatinine measurement, sex, race, and age.
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