Predictability of Some Pregnancy Outcomes Based on SVM and Dichotomous Regression Techniques

Guidi, G.; Adembri, G.; Vannuccini, Silvia; Iadanza, Ernesto

doi:10.1007/978-3-319-13105-4_25

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Although the work was limited to the phonemes of the Tamil language only, the procedure for evaluation is useful in the classification of maternal care problems. The author in [32] compared SVM and Logistic Regression (LR) to determine their performance efficiency in pregnancy outcome prediction on anonymized dataset of 420 different pregnancy details. Four output categories were defined, and the results show that the average specificity of SVM in all four categories is at least 1% higher than that for LR, except in the case of underweight infant prediction where LR had a higher specificity.…”

Section: Classification Approaches For Medical Diagnosticmentioning

confidence: 99%

Optimality Assessments of Classifiers on Single and Multi-labelled Obstetrics Outcome Classification Problems

Udo¹,

Samuel²,

Funebi³

et al. 2021

IJACSA

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It is indisputable that clinicians cannot exactly state the outcome of pregnancies through conventional knowledge and methods even as the surge in human knowledge continues. Hence, several computational techniques have been adapted for precise pregnancy outcome (PO) prediction. Obstetric datasets for PO determination exist as single label learning (SLL), multi-label learning (MLL) and multi-target (MTP) problems. There is however no single classifier recommended to optimally satisfy the needs of all the classification types. This work therefore identifies six widely used PO classifiers and investigates their performances in all three classification categories; to find the best performing classifier. Obstetric dataset exposed to input rank analysis via Principal component Analysis, produced thirteen (13) significant features for the experiment. Accuracy, F1-measure and build/test time were used as evaluation metrics. Decision tree (DT) had an average accuracy and F1 score of 89.23% and 88.23% respectively, with 1.0 average rank. Under MLL configuration, average accuracy (91.71%) and F1 score (94.28%) were highest in the random forest (RF) which had a 1.0 average test time rank. Using MTP, DT had an average accuracy of 88.80% and average F1 score of 71.13%, the multi-layered perceptron (MLP) had the best time cost with an average rank value of 2.0. From the results, RF is most optimal in terms of accuracy and average rank value, while DT is the most efficient in terms of time cost. The comparative analysis of global averages of the six base classifiers shows that RF is the most optimal algorithm with an average accuracy of 87.3% given all three data setups in the study. MLP on the other hand had an unexpectedly high time cost, making it unsuitable for similar data classifications if time is the main criterion. It is recommended that the choice of the classifier should either be RF or DT depending on the application domain and whether or not time cost is a major consideration.

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Section: Classification Approaches For Medical Diagnosticmentioning

confidence: 99%

Optimality Assessments of Classifiers on Single and Multi-labelled Obstetrics Outcome Classification Problems

Udo¹,

Samuel²,

Funebi³

et al. 2021

IJACSA

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“…In more specific cases of pregnancy health, the machine learning algorithm indicates promising performance which is beneficial for the detection of high-risk pregnancy [5], [6] and pregnancy health services in general [7]. Furthermore, machine learning algorithm is also increasingly popular, implemented in various tasks with various data sources in the world of healthcare [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Giving more insight for automatic risk prediction during pregnancy with interpretable machine learning

2021

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Maternal mortality rate (MMR) in Indonesia intercensal population survey (SUPAS) was considered high. For pregnancy risk detection, the public health center (puskesmas) applies a Poedji Rochjati screening card (KSPR) demonstrating 20 features. In addition to KSPR, pregnancy risk monitoring has been assisted with a pregnancy control card. Because of the differences in the number of features between the two control cards, it is necessary to make agreements between them. Our objectives are determining the most influential features, exploring the links among features on the KSPR and pregnancy control cards, and building a machine learning model for predicting pregnancy risk. For the first objective, we use correlation-based feature selection (CFS) and C5.0 algorithm. The next objective was answered by the union operation in the features produced by the two techniques. By performing the machine learning experiment on these features, the accuracy of the XGBoost algorithm demonstrated the hightest results of 94% followed by random forest, Naïve Bayes, and k-Nearest neighbor algorithms, 87%, 66%, and 60% respectively. Interpretability aspects are implemented with SHAP and LIME to provide more insight for classification model. In conclusion, the similarity feature generated in the two interpretation approaches confirmed that Cesar was dominant in determining pregnancy risk.

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Fuzzy Analysis of Delivery Outcome Attributes for Improving the Automated Fetal State Assessment

Czabański

Jeżewski

Horoba

et al. 2016

Applied Artificial Intelligence

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Predictability of Some Pregnancy Outcomes Based on SVM and Dichotomous Regression Techniques

Cited by 3 publications

References 5 publications

Optimality Assessments of Classifiers on Single and Multi-labelled Obstetrics Outcome Classification Problems

Optimality Assessments of Classifiers on Single and Multi-labelled Obstetrics Outcome Classification Problems

Giving more insight for automatic risk prediction during pregnancy with interpretable machine learning

Fuzzy Analysis of Delivery Outcome Attributes for Improving the Automated Fetal State Assessment

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