Exploring Machine Learning Algorithms to Find the Best Features for Predicting Modes of Childbirth

Islam, Muhammad Nazrul; Mahmud, Tahasin; Khan, Nafiz Imtiaz; Mustafina, Sumaiya Nuha; Islam, A. K. M. Najmul

doi:10.1109/access.2020.3045469

Cited by 34 publications

(16 citation statements)

References 37 publications

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“…For example, Abraham, Abin, et al described a new technique for gathering various information from EHRs in order to predict singleton preterm birth by applying various machine learning models [7]. Recently, Islam, Muhammad Nazrul, et al has presented research regarding childbirth mode with two-fold findings: first, the potential highlights for deciding the method of labor, and second, machine learning algorithms for anticipating the suitable way of labor (vaginal birth, crisis cesarean, cesarean birth) [8]. Kowsher, M. et al has reported good accuracy in applying machine learning-based recommendation system to predict the most appropriate childbirth mode [9].…”

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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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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“…Machine Learning (ML) algorithms gain insight from training data samples and can predict the class label of the unknown data objects. ML algorithms are popularly being used in the field of health-informatics [3]- [5], forecasting pandemic [6], evaluating user experience in playing games [7], predicting shear strength [8]. Similarly, many MLbased studies are conducted on medical images to classify brain tumors [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

VGG-SCNet: A VGG Net-Based Deep Learning Framework for Brain Tumor Detection on MRI Images

Majib¹,

Rahman²,

Sazzad³

et al. 2021

IEEE Access

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131

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A brain tumor is a life-threatening neurological condition caused by the unregulated development of cells inside the brain or skull. The death rate of people with this condition is steadily increasing. Early diagnosis of malignant tumors is critical for providing treatment to patients, and early discovery improves the patient's chances of survival. The patient's survival rate is usually very less if they are not adequately treated. If a brain tumor cannot be identified in an early stage, it can surely lead to death. Therefore, early diagnosis of brain tumors necessitates the use of an automated tool. The segmentation, diagnosis, and isolation of contaminated tumor areas from magnetic resonance (MR) images is a prime concern. However, it is a tedious and time-consuming process that radiologists or clinical specialists must undertake, and their performance is solely dependent on their expertise. To address these limitations, the use of computer-assisted techniques becomes critical. In this paper, different traditional and hybrid ML models were built and analyzed in detail to classify the brain tumor images without any human intervention. Along with these, 16 different transfer learning models were also analyzed to identify the best transfer learning model to classify brain tumors based on neural networks. Finally, using different state-of-the-art technologies, a stacked classifier was proposed which outperforms all the other developed models. The proposed VGG-SCNet's (VGG Stacked Classifier Network) precision, recall, and f1 scores were found to be 99.2%, 99.1%, and 99.2% respectively.

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“…Child delivery can be performed through several methods in hospitals, but the most common methods are either traditional vaginal birth or cesarean (c-section), while vacuum extractions and obstetric pincers can be used during complications in vaginal deliveries [5]. There are several assumptions pertaining to the mode of delivery, but it is still challenging to predict the type of childbirth accurately [6,7]. C-section is a technique used in maternity care for delivering children by performing a surgical incision to the woman's abdomen and uterus [8], which normally takes place when complications arise related to the mother or a child in a normal delivery [9].…”

Section: Introductionmentioning

confidence: 99%

“…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. Every mode of delivery has its pros and cons, but selecting the wrong type may lead to a variety of risks such as baby cessation, excessive bleeding, baby breathing problems, and other similar issues [7].…”

Section: Introductionmentioning

confidence: 99%

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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Exploring Machine Learning Algorithms to Find the Best Features for Predicting Modes of Childbirth

Cited by 34 publications

References 37 publications

Predicting the Appropriate Mode of Childbirth using Machine Learning Algorithm

Predicting the Appropriate Mode of Childbirth using Machine Learning Algorithm

VGG-SCNet: A VGG Net-Based Deep Learning Framework for Brain Tumor Detection on MRI Images

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

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