Prediction of Cesarean Childbirth using Ensemble Machine Learning Methods

Khan, Nafiz Imtiaz; Mahmud, Tahasin; Islam, Muhammad Nazrul; Mustafina, Sumaiya Nuha

doi:10.1145/3428757.3429138

Cited by 27 publications

(16 citation statements)

References 18 publications

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“…Their models have predicted c-sections with 96% accuracy for women who had a history of c-sections compared to 89% accuracy for women who had no previous c-section. Khan et al [10] presented a study to predict whether c-section is compulsory along with increased safety for both mother and child during and after delivery. They trained 3 ensemble models and found the highest accuracy model of 87.66%.…”

Section: Related Workmentioning

confidence: 99%

“…AdaBoost is an iterative-based technique where each iteration detects the misclassified data points and increases the weights of the correct points to increase the chance of the next classifiers getting them right. Moreover, in this method, the instances are moved from the iterative samples of the training data to the subsequent data set, and the classifiers are combined based on the weighted majority of votes [10,42].…”

Section: Adaboostmentioning

confidence: 99%

“…The possible complications of c-section for mothers are infections, excessive bleeding that could cause anemia, and reaction to anesthesia; therefore, maternal death rates with c-sections are higher than that for vaginal deliveries [8]. However, a c-section could be necessary to save the lives of both the mother and the child if the baby is located in a wrong position in the womb, the head of the baby is larger than the birth canal, the direction of the baby is reversed, or the mother has a c-section history or even heart-related diseases [10]. Molina et al [11] further explained that c-sections are lifesaving for obstructed labor and any other obstruction in the delivery process for decreasing baby and mother mortality, but the risk of complications and overuse can harm both mothers and babies.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study

Ullah¹,

Saleem²,

Jamjoom³

et al. 2021

J Med Internet Res

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Background The use of artificial intelligence has revolutionized every area of life such as business and trade, social and electronic media, education and learning, manufacturing industries, medicine and sciences, and every other sector. The new reforms and advanced technologies of artificial intelligence have enabled data analysts to transmute raw data generated by these sectors into meaningful insights for an effective decision-making process. Health care is one of the integral sectors where a large amount of data is generated daily, and making effective decisions based on these data is therefore a challenge. In this study, cases related to childbirth either by the traditional method of vaginal delivery or cesarean delivery were investigated. Cesarean delivery is performed to save both the mother and the fetus when complications related to vaginal birth arise. Objective The aim of this study was to develop reliable prediction models for a maternity care decision support system to predict the mode of delivery before childbirth. Methods This study was conducted in 2 parts for identifying the mode of childbirth: first, the existing data set was enriched and second, previous medical records about the mode of delivery were investigated using machine learning algorithms and by extracting meaningful insights from unseen cases. Several prediction models were trained to achieve this objective, such as decision tree, random forest, AdaBoostM1, bagging, and k-nearest neighbor, based on original and enriched data sets. Results The prediction models based on enriched data performed well in terms of accuracy, sensitivity, specificity, F-measure, and receiver operating characteristic curves in the outcomes. Specifically, the accuracy of k-nearest neighbor was 84.38%, that of bagging was 83.75%, that of random forest was 83.13%, that of decision tree was 81.25%, and that of AdaBoostM1 was 80.63%. Enrichment of the data set had a good impact on improving the accuracy of the prediction process, which supports maternity care practitioners in making decisions in critical cases. Conclusions Our study shows that enriching the data set improves the accuracy of the prediction process, thereby supporting maternity care practitioners in making informed decisions in critical cases. The enriched data set used in this study yields good results, but this data set can become even better if the records are increased with real clinical data.

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Section: Related Workmentioning

confidence: 99%

Section: Adaboostmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study

Ullah¹,

Saleem²,

Jamjoom³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Kowsher, M. et al has reported good accuracy in applying machine learning-based recommendation system to predict the most appropriate childbirth mode [9]. Also, Khan, Nafiz Imtiaz, et al have a similar test to anticipate whether the cesarean area is essential with the assistance of information mining and subsequently expand the mother and infant's security during and after labor by staying away from a pointless cesarean segment [10]. Besides, Fu, Yuanqing, et al had described a model to recognize early life hazard factors for youth overweight/stoutness among preterm babies and decided to take care of practices that could alter the distinguished danger factors [11].…”

Section: Related Workmentioning

confidence: 99%

Predicting the Appropriate Mode of Childbirth using Machine Learning Algorithm

Kowsher¹,

Prottasha²,

Tahabilder³

et al. 2021

IJACSA

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A woman's satisfaction with childbirth may have immediate and long-term effects on her health as well as on the relationship with her newborn child. The mode of baby delivery is genuinely vital to a delivery patient and her infant child. It might be a crucial factor for ensuring the safety of both the mother and the child. During the baby delivery, decision-making within a short time becomes very challenging for the physician. Besides, humans may make wrong decisions selecting the appropriate delivery mode of childbirth. A wrong decision increases the mother's life risk and can also be harmful to the newborn baby's health. Computer-aided decision-making can be an excellent solution to this problem. Considering this scope, we have built a supervised machine learning-based decision-making model to predict the most suitable childbirth mode that will reduce this risk. This work has applied 32 supervised classifier algorithms and 11 training methods on the real childbirth dataset from the Tarail Upazilla Health complex, Kishorganj, Bangladesh. We have also analyzed the result and compared them using various statistical parameters to determine the bestperformed model. The quadratic discriminant analysis has shown the highest accuracy of 0.979992 with the F1 score of 0.979962. Using this model to decide the appropriate labor mode may significantly reduce maternal and infant health risks.

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“…Machine learning (ML) algorithms learn the hidden pattern of the data and can predict the class labels of unknown samples. Thus, ML is widely being used in the field of health informatics [11] [12] [13] [14], forecasting pandemic [15], predicting shear strength [16], etc. Similarly, ML is widely being used in the field of sentiment analysis for predicting public sentiment [17] [18].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 and Black Fungus: Analysis of the Public Perceptions through Machine Learning

Islam¹,

Khan²,

Mahmud³

2021

Preprint

Self Cite

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While COVID-19 is ravaging the lives of millions of people across the globe, a second pandemic ‘black fungus’ has surfaced robbing people of their lives especially people who are recovering from coronavirus. Again, the public perceptions regarding such pandemics can be investigated through sentiment analysis of social media data. Thus the objective of this study is to analyze public perceptions through sentiment analysis regarding black fungus during the time of the COVID-19 pandemic. To attain the objective, first, a Support Vector Machine model, with an average AUC of 82.75%, was developed to classify user sentiments in terms of anger, fear, joy, and sad. Next, this Support Vector Machine is used to supervise the class labels of the public tweets (n = 6477) related to COVID-19 and black fungus. As outcome, this study found that public perceptions belong to sad (n = 2370, 36.59 %), followed by joy (n = 2095, 32.34%), fear (n = 1914, 29.55 %) and anger (n = 98, 1.51%) towards black fungus during COVID-19 pandemic. This study also investigated public perceptions of some critical concerns (e.g., education, lockdown, hospital, oxygen, quarantine, and vaccine) and it was found that public perceptions of these issues varied. For example, for the most part, people exhibited fear in social media about education, hospital, vaccine while some people expressed joy about education, hospital, vaccine, and oxygen.

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Prediction of Cesarean Childbirth using Ensemble Machine Learning Methods

Cited by 27 publications

References 18 publications

Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study

Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study

Predicting the Appropriate Mode of Childbirth using Machine Learning Algorithm

COVID-19 and Black Fungus: Analysis of the Public Perceptions through Machine Learning

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