Beschreibung
Conventionally, physicians follow a cognitive decision making process, the appropriateness of which develops with experience and knowledge gained from literature, lectures etc. Having inadequate knowledge or experience may lead to misdiagnosis of diseases consequently affecting the patient physically, emotionally, financially and so on. Using data mining techniques, it is possible to develop accurate diagnostic models that can be used in clinical decision making. Adopting such models in clinical practice may ensure better decision making thereby decreasing the rate of misdiagnosis, minimising the uneasiness, pain and anxiety that is associated with a disease through early detection and treatment. In this thesis, (i) the performance of standard classification algorithms in CKD detection was explored (ii) a new hybrid approach to accurately diagnose CKD is presented and (iii) the application of distributed random forest algorithm for developing generalized model for CKD diagnosis was proposed.
Autorenporträt
Dr. Klinsega Jeberson is serving as Assistant Professor in Sam Higginbottom University of Agriculture, Technology and Sciences (SHUATS), India. She has more than 10 years of experience in teaching and research in the field of Data Mining with several research publications. She has received a Ph.D. in Computer Applications from SHUATS.
Herstellerkennzeichnung:
OmniScriptum SRL
Str. Armeneasca 28/1, office 1
2012 Chisinau
MD
E-Mail: info@omniscriptum.com
