A Study on C.5 Decision Tree Classification Algorithm for Risk Predictions During Pregnancy. (2016)

• Record Type: Journal Article
• Authors: Lakshmi, B.N.; Indumathi, T.S.; Ravi, Nandini
• Is Part Of: Procedia technology. Volume 24(2016)
• Page Start: 1542
• Page End: 1549
• Publication Date: 2016
• Subjects: pregnancy -- learning models -- C4.5 classification algorithm -- pregnancy complications -- abnormalities -- standardized dataset -- un-standardized dataset; Technology -- Congresses; Technology -- Periodicals; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Technology; Conference proceedings; Periodicals; 605
• DOI: 10.1016/j.protcy.2016.05.128
• Languages: English
• ISSNs: 2212-0173

Abstract

Abstract: Complication during pregnancy has turned out o be a major problem for women of today's era. Pregnant women must be protected from these complications arising in period of gestation, a stage wherein every woman undergoes many physiological changes, sometimes inducing severe health problems leading to death of both mother and fetus. Technological interventions in the field of medical diagnosis can largely help to find a solution for this problem to protecting pregnant women, thus in turn reducing maternal and fetal mortalities to great extents. Decision Tree Classification method is a popularly used method whose algorithms are best suitable in medical diagnosis. C4.5 Decision Tree algorithm is one of the popular and effectively used classifier for pregnancy data classification in present study. The main aim of this paper is to pinpoint the importance of standardization of parameters selected for data collection in study, compare the results obtained from C4.5 classifier on both un-standardized and standardized datasets and analyse the performance of the C4.5 algorithm in terms of its prediction accuracy when applied on the created database from collected and standardized pregnancy data.