Decision Tree Method Using for Fetal State Classification from Cardiotography Data

Arif, Zannatul; Ahmed, Rahate; Sadia, Umma Habiba; Tultul, Mst Shanta Islam; Chakma, Rocky

doi:10.25073/jaec.202041.273

Cited by 16 publications

(12 citation statements)

References 2 publications

(2 reference statements)

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“…The next task, after getting important features from the infant dataset, is to build classification models to train the machine so that it can predict whether an infant will survive or not, depending upon the selected features. In this research, we have used six state-of-the-art classification techniques, namely Logistic Regression [22], KNN [22], Decision Tree [23], Random Forest (RF) [24], Gaussian Naive Bayes, 3 and Support Vector Machine [5], [24] to classify the reduced datasets. During the analysis of the infant data set, every algorithm was checked with ten-fold crossvalidation.…”

Section: E Classificationmentioning

confidence: 99%

Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches

et al. 2022

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The rate of infant mortality (IMR) in a population under one year of age is a marker for infant mortality. It is a major sensitive marker of a community's overall physical health. Protecting the lives of newborns has become a challenging issue in public health, development programs, and humanitarian initiatives. Almost 10.1% infants died in the United States of America (USA) in 2021. Therefore, this paper aims to extract and understand the various influential factors causing infant deaths in the USA. A crowding distance-based multi-objective ant lion optimization (MOALO-CD) is proposed here with statistical evidence for feature selection. The proposed technique is compared with competitive metaheuristic models such as multi-objective genetic algorithm based on crowding distance (MOGA-CD), multi-objective filter approaches, and recursive feature elimination. Various machine learning classifiers are applied to the selected feature subset obtained from MOALO-CD on the USA's infant dataset. Extensive experimental results indicate that the proposed model outperforms the existing metaheuristic approaches in terms of Generational Distance, Inverted Generational Distance, Spread, and Hyper volume. Also, the comparative analysis of various machine learning models reveals that random forest achieves significantly better performance on the feature subset obtained from MOALO-CD.

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Section: E Classificationmentioning

confidence: 99%

Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches

et al. 2022

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“…Menurut [4], menyimpulkan pohon keputusan dapat melakukan Cardiotocography fitur seleksi dan mengklasifikasikan risiko kehamilan dengan tingkat akurasi yang baik. Fitur seleksi terbukti cukup baik dan mampu meningkatkan akurasi hasil hingga 98,7%.…”

Section: Pendahuluanunclassified

“…Perancangan klasifikasi pada kasus kesehatan janin mendapatkan data melalui CTG, dimana menggunakan Algoritma C5.0 berdasarkan pohon keputusan dan aturan terkait kondisi janin menggunakan persamaan (1)(2)(3)(4). Entropy dapat menentukan seberapa informatif atribut yang akan digunakan.…”

Section: Pendahuluanunclassified

Rekomendasi Kesehatan Janin Dengan Penerapan Algoritma C5.0 Menggunakan Classifying Cardiotocography Dataset

Santoso¹,

Musa

2021

J.Simantec

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Memiliki buah hati yang sehat tidak luput dari faktor kesehatan sang ibu dan kondisi janin di dalam rahim, sehingga butuh analisa terhadap kesehatan janin pada setiap ibu hamil. Penelitian ini mengusulkan Algoritma C5.0 memanfaatkan dataset Cardiotocography terkait kondisi janin. Dataset Cardiotocography terdiri dari 2.126 record dengan, dimana setiap record memiliki 22 kolom atribut dan terdapat 3 kelas klasifikasi, yaitu; normal, suspect, dan pathological. Dengan menghitung entropy, information gain, split information, dan gain ratio, serta menggunakan confusion matrix sebagai perbandingan akurasi dari data yang diteliti. Kelompok record dibagi menjadi data training dan data testing dengan variasi 60%, 70%, 80%, dan 90% untuk data training dan dengan 40%, 30%, 20%, 10% untuk data testing. Hasil dari rekomendari pada variasi pembagian 90% data training dan 10% data testing menghasilkan akurasi sebesar 93,40% dengan jumlah aturan sebanyak 257. Sedangkan variasi 80% data training dan 20% data testing menghasilkan akurasi sebesar 91,29% dengan jumlah aturan sebanyak 239. Pada variasi 70% data training dan 30% data testing menghasilkan akurasi sebesar 88,23% dengan jumlah aturan sebanyak 220. Dan variasi pembagian terkecil 60% data training dan 40% data testing menghasilkan akurasi sebesar 88,12% dengan jumlah aturan sebanyak 204. Berdasarkan variasi tersebut, maka dapat disimpulkan semakin besarnya data training akan menyebabkan akurasi menjadi semakin baik dengan jumlah aturanaturan yang dapat berguna untuk dijadikan sebagai sistem penunjang keputusan.

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“…In order to assist the obstetricians, prediction algorithm may perform exceptionally well and assist in monitoring the fetal health state. In order to monitor the foetal health condition, the antenatal care CTG test is typically performed after the following 28th week (during the 7th month of pregnancy) [2]. Before 28 weeks of pregnancy, there is limited brain activity because the central nervous system is not fully developed.…”

Section: Introductionmentioning

confidence: 99%

Stack Ensemble Algorithm for Cardiotocography Based Foetal Health Risk Classification

Nagabotu,

Namburu

2024

RIA

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Obstetricians utilize cardiotocography (CTG) to assess the fetal heart and lungs during pregnancy. It can help determine if the foetus is healthy, in doubt, or suffering from disease by providing data on the fetal heart rate and uterine breathing. The analysis of CTG data has typically made use of machine learning (ML) techniques such as support vector machines and decision trees to forecast fetal health and enhance the detection procedure. Fetal heart rate and uterine contraction timing were recorded by CTG. Monitoring fetal health and ensuring normal fetal growth and development throughout pregnancy rely heavily on CTG intelligent categorization. Pregnancies with a higher risk of problems are the most common cases in which CTG is used to evaluate the health of the fetus. ML algorithms are utilized to evaluate state of the foetal health based on CTG-obtained factors. Compared to ML techniques, ensemble models have been shown to increase detection speed and effectiveness. Ensemble modeling refers to the practice of combining the scores or distributions from multiple related but distinct analytical models. In order to predict foetal health, ensemble models such as Boosting, AdaBoost, Extreme Gradient Boosting, Light gradient boost method (LightGBM), and stack models were used in the paper. When the outcomes are compared, the proposed stack model using logistic regression, decision tree, random forest and LightGBM proved to obtain the best performance with 96.71% success rate. The proposed methodology, which can be used to classify foetal health based on Fetal Heart Rate (FHR) data, is more efficient and superior to existing machine learning models, which have already been taken into consideration.

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Decision Tree Method Using for Fetal State Classification from Cardiotography Data

Cited by 16 publications

References 2 publications

Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches

Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches

Rekomendasi Kesehatan Janin Dengan Penerapan Algoritma C5.0 Menggunakan Classifying Cardiotocography Dataset

Stack Ensemble Algorithm for Cardiotocography Based Foetal Health Risk Classification

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