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The obtained data from these recordings can be utilized to anticipate the condition of the newborn baby, which gives an open door for early medication before perpetual deficiency to the fetus. The dimension of the obtained features from CTG is high and decreases the accuracy of classification algorithms. In this article, supervised particle swarm optimization (PSO) with a rough set-based dimensionality reduction method is used to find a minimal set of significant features from CTG extracted features. The proposed weighted rough set classifier (WRSC) method is utilized for predicting the fetal condition as normal and pathological states. The performance of the proposed WRSC algorithm is compared with various classification algorithms such as bijective soft set neural network classifier (BISONN), rough set-based classifier (RST), multi-layered perceptron (MLP), decision table (DT), Java repeated incremental pruning (JRIP) classifier, J48 and Na\u00efve Bayes (NB) classifiers. The experimental results demonstrated that the proposed algorithm is capable of forecasting the fetal state with 98.5% classification accuracy, and the results show that the proposed classification algorithm performed considerably superior than other classification techniques.<\/jats:p>","DOI":"10.4018\/ijskd.2019100101","type":"journal-article","created":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T18:25:57Z","timestamp":1579285557000},"page":"1-19","source":"Crossref","is-referenced-by-count":15,"title":["Weighted Rough Set Theory for Fetal Heart Rate Classification"],"prefix":"10.4018","volume":"11","author":[{"given":"S. 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