E. M. Karabulut, T. Ibrikci
problem from different area, can fail for a different problem. Therefore, search should be broadened for a com-
puter solution especially for a medical decision. Therefore, the results of prior studies are considered in our
analysis of CTG. The determination of state of fetus is especially important for early intervention of required
cases, i.e. fetal distress or preventing unnecessary surgeries.
The effect of using AdaBoost ensemble on classifiers is investigated for perfect determination of fetal distress
from CTG data in this study. Figure 2 visually represents the promising results of experiments related to contri-
bution of AdaBoost ensemble on classifying machine learning algorithms, confirming the fact that ensemble
machine learning approaches often performs much better than single classifiers that make them up [12]. The
most prominent result belongs to decision tree based AdaBoost algorithm by 0.034 MAE, 0.861 kappa statistics
and 95.01% accuracy, meaning that 2020 of 2126 samples are perfectly predicted. These results are an improved
next step following the related studies carried out in literature.
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