FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3198897768> ?p ?o ?g. }

• W3198897768 endingPage "126501" @default.
• W3198897768 startingPage "126481" @default.
• W3198897768 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. The experiments showed that SHAP (SHapley Additive exPlanations) feature selection method has extracted better features than the other 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." @default.
• W3198897768 created "2021-09-13" @default.
• W3198897768 creator A5026775595 @default.
• W3198897768 creator A5027854717 @default.
• W3198897768 creator A5038463167 @default.
• W3198897768 creator A5044273137 @default.
• W3198897768 creator A5048369170 @default.
• W3198897768 creator A5062716310 @default.
• W3198897768 creator A5070171033 @default.
• W3198897768 date "2021-01-01" @default.
• W3198897768 modified "2023-10-17" @default.
• W3198897768 title "A Comprehensive Unsupervised Framework for Chronic Kidney Disease Prediction" @default.
• W3198897768 cites W1967492795 @default.
• W3198897768 cites W2028601355 @default.
• W3198897768 cites W2038105731 @default.
• W3198897768 cites W2051224630 @default.
• W3198897768 cites W2068078765 @default.
• W3198897768 cites W2106296294 @default.
• W3198897768 cites W2113586398 @default.
• W3198897768 cites W2123296398 @default.
• W3198897768 cites W2167914628 @default.
• W3198897768 cites W2171073331 @default.
• W3198897768 cites W2234357320 @default.
• W3198897768 cites W2270655762 @default.
• W3198897768 cites W2593330790 @default.
• W3198897768 cites W2735242549 @default.
• W3198897768 cites W2735916432 @default.
• W3198897768 cites W2754384272 @default.
• W3198897768 cites W2795475639 @default.
• W3198897768 cites W2928048063 @default.
• W3198897768 cites W2933013505 @default.
• W3198897768 cites W2940010972 @default.
• W3198897768 cites W2940837674 @default.
• W3198897768 cites W2948149587 @default.
• W3198897768 cites W2955086442 @default.
• W3198897768 cites W2955796858 @default.
• W3198897768 cites W2968767342 @default.
• W3198897768 cites W2969631555 @default.
• W3198897768 cites W2972606465 @default.
• W3198897768 cites W2978484237 @default.
• W3198897768 cites W3002028984 @default.
• W3198897768 cites W3004971492 @default.
• W3198897768 cites W3011408237 @default.
• W3198897768 cites W3044853528 @default.
• W3198897768 cites W3094177185 @default.
• W3198897768 cites W3098049961 @default.
• W3198897768 cites W3100118890 @default.
• W3198897768 cites W3107425599 @default.
• W3198897768 cites W3109369928 @default.
• W3198897768 cites W3135863637 @default.
• W3198897768 cites W3172921504 @default.
• W3198897768 cites W325781386 @default.
• W3198897768 doi "https://doi.org/10.1109/access.2021.3109168" @default.
• W3198897768 hasPublicationYear "2021" @default.
• W3198897768 type Work @default.
• W3198897768 sameAs 3198897768 @default.
• W3198897768 citedByCount "17" @default.
• W3198897768 countsByYear W31988977682022 @default.
• W3198897768 countsByYear W31988977682023 @default.
• W3198897768 crossrefType "journal-article" @default.
• W3198897768 hasAuthorship W3198897768A5026775595 @default.
• W3198897768 hasAuthorship W3198897768A5027854717 @default.
• W3198897768 hasAuthorship W3198897768A5038463167 @default.
• W3198897768 hasAuthorship W3198897768A5044273137 @default.
• W3198897768 hasAuthorship W3198897768A5048369170 @default.
• W3198897768 hasAuthorship W3198897768A5062716310 @default.
• W3198897768 hasAuthorship W3198897768A5070171033 @default.
• W3198897768 hasBestOaLocation W31988977681 @default.
• W3198897768 hasConcept C119857082 @default.
• W3198897768 hasConcept C120665830 @default.
• W3198897768 hasConcept C121332964 @default.
• W3198897768 hasConcept C126322002 @default.
• W3198897768 hasConcept C138885662 @default.
• W3198897768 hasConcept C154945302 @default.
• W3198897768 hasConcept C159641895 @default.
• W3198897768 hasConcept C160735492 @default.
• W3198897768 hasConcept C162324750 @default.
• W3198897768 hasConcept C177713679 @default.
• W3198897768 hasConcept C194051981 @default.
• W3198897768 hasConcept C2778137410 @default.
• W3198897768 hasConcept C2778653478 @default.
• W3198897768 hasConcept C2779134260 @default.
• W3198897768 hasConcept C2988170871 @default.
• W3198897768 hasConcept C41008148 @default.
• W3198897768 hasConcept C41895202 @default.
• W3198897768 hasConcept C50522688 @default.
• W3198897768 hasConcept C61511704 @default.
• W3198897768 hasConcept C71924100 @default.
• W3198897768 hasConceptScore W3198897768C119857082 @default.
• W3198897768 hasConceptScore W3198897768C120665830 @default.
• W3198897768 hasConceptScore W3198897768C121332964 @default.
• W3198897768 hasConceptScore W3198897768C126322002 @default.
• W3198897768 hasConceptScore W3198897768C138885662 @default.
• W3198897768 hasConceptScore W3198897768C154945302 @default.
• W3198897768 hasConceptScore W3198897768C159641895 @default.
• W3198897768 hasConceptScore W3198897768C160735492 @default.
• W3198897768 hasConceptScore W3198897768C162324750 @default.
• W3198897768 hasConceptScore W3198897768C177713679 @default.

• next