A comparative study of classification algorithms for predicting gestational risks in pregnant women

Lakshmi, Baddireddi Subhadra; Indumathi, T. S.; Ravi, Nandini

doi:10.1109/ccoms.2015.7562849

Cited by 10 publications

(4 citation statements)

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“…Tıbbi teşhis ve tahminde makine öğrenimi üzerine yapılan önceki araştırmalara bakıldığında; C4.5 Karar Ağacı, Naive Bayes algoritmaları ile hamilelik risklerini ve hamileliğin normal veya anormal evrelerini tahmin etmek üzere çalışmalar yapılmıştır (Lakshmi, Indumathi, & Ravi, 2015). Ahmet vd.…”

Section: Kaynak öZetleri̇unclassified

Mühendislik Alanında Akademik Analiz ve Tartışmalar

2023

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Mühendislik bilim, teknoloji ve matematiği birbirine bağlamayı amaçlar ve bir disiplin olarak binlerce yıldır uygulanmaktadır. Mühendisler, hücre zarlarından inşaat ve protezlere, motor ve taşıma verimliliğinin iyileştirilmesine ve yenilenebilir enerji kaynaklarının geliştirilmesine kadar her konuda çalışırlar. Mühendislik, tekerleğin icadına (ve ötesine) kadar uzanıyor olsa da, terimin kendisi, bir "mühendis"in mancınık ve diğer "kuşatma makineleri" gibi askeri motorlar inşa eden kişi anlamına geldiği 14. yüzyıla kadar uzanmaktadır. Mühendislik, askeri uygulamaların ötesinde gelişerek köprüler ve binalar gibi sivil yapılara uygulanmaya başlanmıştır. Bu da onu orijinal askeri mühendislik alanından ayırmak için inşaat mühendisliği teriminin yaratılmasına yol açmıştır. Bu kitapta mühendislik bilimleri incelenmiştir.

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Section: Kaynak öZetleri̇unclassified

Mühendislik Alanında Akademik Analiz ve Tartışmalar

2023

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“…Alisha Kamat et al [7] proposed a prediction model using Naive Bayes and ID3 classifiers to determine the type of delivery based on ultrasonography, urine and blood reports of pregnant women. ML techniques like a decision tree and Naïve Bayes were applied for the prediction of pregnancy-related risk factors [8] and to predict normal or abnormal stages of pregnancy [9]. A classification model was proposed, and it allows an estimation of the interval for the value of the Apgar score depending on mother and newborn data [10].…”

Section: Literature Surveymentioning

confidence: 99%

Analysis of Association between Caesarean Delivery and Gestational Diabetes Mellitus Using Machine Learning

Prema¹,

Pushpalatha²

2020

Proc. eng. technol. innov.

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The study aims to analyze the association between gestational diabetes mellitus (GDM) and other risk factors of cesarean delivery using machine learning (ML). The dataset used for the analysis is from the pregnancy risk assessment survey (PRAMS), considered in two scenarios, i.e., all the data is taken, and all the data of the women who developed GDM. Further, the data is developed in two groups Data-I and Data-II by considering multiparous and primiparous women details, respectively. The correlation analysis and major classification algorithms are applied to the data. It is founded that the top risk factors for the first time cesarean delivery are the age, height, weight, race of the women, presence of hypertension and gestational diabetes mellitus. The major risk factor for repeated cesarean delivery is the previous cesarean delivery. The presence of GDM is also one of the risk factors for cesarean delivery.

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“…The Naïve Bayes approach was observed to show better performance as compared to ID3. B. N. Lakshmi et al [19] predicted the risk levels during pregnancy with accuracy of 71.30% for the standardized data and 66.08% for non-standardized data. M. W. L. Moreira et al [20] in their study found that the TAN classifier produced the best results in terms of accuracy for predicting four hypertensive disorders during pregnancy than Averaged One Dependence Estimator (AODE) and Naïve Bayes classifiers.…”

Section: Review Of Literaturementioning

confidence: 99%

Data Mining Probabilistic Classifiers for Extracting Knowledge from Maternal Health Datasets

Sourabh*,

Mansotra

2019

IJITEE

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Data Mining is an important sub-process of Knowledge Discovery in Databases (KDD) or Knowledge Discovery Process (KDP) methodology that is mainly used for applying various data mining techniques and algorithms on the target data. In this research paper, the authors have made an attempt to discover knowledge by classifying the maternal healthcare data of Jammu and Kashmir State of India (now declared as Union Territory by the Government of India). The data for the present research work was collected from a web portal named as Health Management Information System (HMIS) facilitated by Ministry of Health and Family Welfare (MoHFW), Government of India. The data consists of diverse health parameters pertaining to the maternal health of women and for this study, the maternal healthcare data of all districts of Jammu and Kashmir State was considered. Two data mining classifiers viz. Bayesian TAN and Naïve Bayes were applied for classifying the districts of Jammu and Kashmir State into High MMR and Low MMR districts based on the available past data from 2014 to 2018. Additionally, evaluation measures viz. Accuracy, F-measure, Area under the Curve (AUC), and Gini have been used to evaluate the performance of the models developed by Bayesian TAN and Naïve Bayes.

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A comparative study of classification algorithms for predicting gestational risks in pregnant women

Cited by 10 publications

References 0 publications

Mühendislik Alanında Akademik Analiz ve Tartışmalar

Mühendislik Alanında Akademik Analiz ve Tartışmalar

Analysis of Association between Caesarean Delivery and Gestational Diabetes Mellitus Using Machine Learning

Data Mining Probabilistic Classifiers for Extracting Knowledge from Maternal Health Datasets

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