Computer Aided Diagnosis for Chronic Kidney Disease Using Data Mining

2019 ◽  
Vol 8 (3) ◽  
pp. 188-193
Author(s):  
Mohammad Ashraf Ottom ◽  
Khalid M. Nahar ◽  
Keyword(s):  
Data Mining ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Using Data ◽  
Aided Diagnosis

scholarly journals Data Mining Technique to Interpret Lung Nodule for Computer Aided Diagnosis

2011 ◽  
pp. 153-158
Author(s):  
Aswini Kumar Mohanty ◽  
Saroj Kumar Lenka
Keyword(s):  
Data Mining ◽  
Diagnostic Criteria ◽  
Lung Nodule ◽  
Computer Aided Diagnosis ◽  
Image Features ◽  
Data Mining Technique ◽  
Cad Systems ◽  
Computer Aided ◽  
Using Data ◽  
Aided Diagnosis

Diagnostic decision-making in pulmonary medical imaging has been improved by computer-aided diagnosis (CAD) systems, serving as second readers to detect suspicious nodules for diagnosis by a radiologist. Though increasing the accuracy, these CAD systems rarely offer useful descriptions of the suspected nodule or their decision criteria, mainly due to lack of nodule data. In this paper, we present a framework for mapping image features to radiologist-defined diagnostic criteria based on the newly available data). Using data mining, we found promising mappings to clinically relevant, human-interpretable nodule characteristics such as malignancy, margin, spiculation, subtlety, and texture. Bridging the semantic gap between computed image features and radiologist defined diagnostic criteria allows CAD systems to offer not only a second opinion but also decision-support criteria usable by radiologists. Presenting transparent decisions will improve the clinical acceptance of CAD.

scholarly journals Computer-Aided Diagnosis of Chronic Kidney Disease in Developing Countries: A Comparative Analysis of Machine Learning Techniques

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 25407-25419 ◽  
Author(s):  
Alvaro Sobrinho ◽  
Andressa C. M. Da S. Queiroz ◽  
Leandro Dias Da Silva ◽  
Evandro De Barros Costa ◽  
Maria Eliete Pinheiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Developing Countries ◽  
Comparative Analysis ◽  
Kidney Disease ◽  
Computer Aided Diagnosis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Optical coherence tomography and computer-aided diagnosis of a murine model of chronic kidney disease

2017 ◽  
Vol 22 (12) ◽  
pp. 1 ◽  
Author(s):  
Bohan Wang ◽  
Hsing-Wen Wang ◽  
Hengchang Guo ◽  
Erik Anderson ◽  
Qinggong Tang ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Optical Coherence Tomography ◽  
Kidney Disease ◽  
Murine Model ◽  
Computer Aided Diagnosis ◽  
Optical Coherence ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 393
Author(s):  
Mahsa Mansourian ◽  
Sadaf Khademi ◽  
Hamid Reza Marateb
Keyword(s):  
Data Mining ◽  
Mental Disorders ◽  
Neurological Disorders ◽  
External Validation ◽  
Computer Aided Diagnosis ◽  
World Health ◽  
Performance Indices ◽  
Life Years ◽  
Computer Aided ◽  
Aided Diagnosis

The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer’s disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies.

Early Prediction of Chronic Kidney Disease Using Data Mining Techniques

2021 ◽  
pp. 947-957
Author(s):  
Hasin Shahed Shad ◽  
Zeeshan Jamal ◽  
S. M. Foysal Ahmed ◽  
Sifat Momen ◽  
Nafees Mansoor
Keyword(s):  
Data Mining ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Early Prediction ◽  
Data Mining Techniques ◽  
Using Data

Chronic Kidney Disease (CKD) Prediction Using Data Mining Techniques

2021 ◽  
pp. 976-988
Author(s):  
Abhijit Pathak ◽  
Most. Asma Gani ◽  
Abrar Hossain Tasin ◽  
Sanjida Nusrat Sania ◽  
Md. Adil ◽  
...  
Keyword(s):  
Data Mining ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Data Mining Techniques ◽  
Using Data

Chronic Kidney Disease Prediction Using Data Mining

2020 ◽  
Author(s):  
J. Snegha ◽  
V. Tharani ◽  
S.Dhivya Preetha ◽  
R Charanya ◽  
S Bhavani
Keyword(s):  
Data Mining ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Disease Prediction ◽  
Using Data

scholarly journals Early Detection of Cardiovascular Disease in Patients with Chronic Kidney Disease using Data Mining Techniques

2020 ◽  
Vol 6 (3) ◽  
pp. 65-76
Author(s):  
Avijit Kumar Chaudhuri ◽  
Arkadip Ray ◽  
Anirban Das ◽  
Prasun Chakrabarti ◽  
Dilip K. Banerjee
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Early Detection ◽  
Data Mining Techniques ◽  
Using Data
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