Maternal mortality classification for health promotive in Dairi using machine learning approach

Manik, Henry M.; Siregar, Muhammad Fidel Ganis; Rochadi, R. Kintoko; Sudaryati, Etti; Yustina, Ida; Triyoga, Rika Subarniati

doi:10.1088/1757-899x/851/1/012055

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…According to Geller et al [62], maternal mortality "is used globally to monitor maternal health, the general quality of reproductive health care, and the progress countries have made toward international development goals". In a quickly investigation, we found only few recent (and incipient) works that focus on maternal mortality [63,64], showing that there are many research opportunities to contribute in this area. This SLR is also essential for the development of new researches.…”

Section: Discussionmentioning

confidence: 99%

On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature

Rocha

Melo

Mello

et al. 2022

BMC Med Inform Decis Mak

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Background Care during pregnancy, childbirth and puerperium are fundamental to avoid pathologies for the mother and her baby. However, health issues can occur during this period, causing misfortunes, such as the death of the fetus or neonate. Predictive models of fetal and infant deaths are important technological tools that can help to reduce mortality indexes. The main goal of this work is to present a systematic review of literature focused on computational models to predict mortality, covering stillbirth, perinatal, neonatal, and infant deaths, highlighting their methodology and the description of the proposed computational models. Methods We conducted a systematic review of literature, limiting the search to the last 10 years of publications considering the five main scientific databases as source. Results From 671 works, 18 of them were selected as primary studies for further analysis. We found that most of works are focused on prediction of neonatal deaths, using machine learning models (more specifically Random Forest). The top five most common features used to train models are birth weight, gestational age, sex of the child, Apgar score and mother’s age. Having predictive models for preventing mortality during and post-pregnancy not only improve the mother’s quality of life, as well as it can be a powerful and low-cost tool to decrease mortality ratios. Conclusion Based on the results of this SRL, we can state that scientific efforts have been done in this area, but there are many open research opportunities to be developed by the community.

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Section: Discussionmentioning

confidence: 99%

On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature

Rocha

Melo

Mello

et al. 2022

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…According to Geller et al [60], maternal mortality "is used globally to monitor maternal health, the general quality of reproductive health care, and the progress countries have made toward international development goals". In a quickly investigation, we found only few recent (and incipient) works that focus on maternal mortality [61,62], showing that there are many research opportunities to contribute in this area.…”

Section: Discussion and Final Considerationsmentioning

confidence: 99%

On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature

Rocha

Melo

Mello

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Care during pregnancy, childbirth and puerperium are fundamental to avoid pathologies for the mother and her baby. However, health issues can occur during this period, causing misfortunes, such as the death of the fetus or neonate. Predictive models of fetal and infant deaths are important technological tools that can help to reduce mortality indexes. The main goal of this work is to present a systematic review of literature focused on computational models to predict mortality, covering stillbirth, perinatal, neonatal, and infant deaths, highlighting their methodology and the description of the proposed computational models. Methods: We conducted a systematic review of literature, limiting the search to the last 10 years of publications considering the five main scientific databases as source. Results: From 671 works, 18 of them were selected as primary studies for further analysis. We found that most of works are focused on prediction of neonatal deaths, using machine learning models (more specifically Random Forest). The top five most common features used to train models are birth weight, gestational age, sex of the child, Apgar score and mother's age. Having predictive models for preventing mortality during and post-pregnancy not only improve the mother's quality of life, as well as it can be a powerful and low-cost tool to decrease mortality ratios. Conclusion: Based on the results of this SRL, we can state that scientific efforts have been done in this area, but there are many open research opportunities to be developed by the community.

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“…In this study case the analysis will be carried out to find the best accuracy in determining the classification of fungi using two classification algorithms. The proposed algorithm is Naive Bayes algorithm [8] and K-Nearest Neighbor (KNN) algorithm [9], then evaluates and validates the results by looking for the best accuracy results of these two algorithms. The next stage is compare the results of the accuracy of each algorithm, to get a model classification algorithm that obtains the highest accuracy and time complexity [10].…”

Section: Methodsmentioning

confidence: 99%

Accuracy of classification poisonous or edible of mushroom using naïve bayes and k-nearest neighbors

Hamonangan¹,

Saputro²,

Atmaja³

2021

joscex

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Mushrooms are plants that are widely consumed by the general public, but not all mushrooms can be consumed directly, because the types of mushrooms are feasible and it is still too difficult to distinguish, then there are several ways to identify fungi, namely by means of morphology. The morphology referred to in this paper is the morphology of fungi which includes color, habitat, class, and others. We got the morphology of this mushroom from a datasets we get from UCI Machine Learning with the 23 atribut that we use in the program. In determining the classification of this fungus we use the Naive Bayes algorithm which produces an accuracy of around 90,2% which we then improve again so that it reaches 100% accuracy using the K-Nearest Neighbors algorithm. Furthermore, in this case to prove accuracy that we had before, we use calculation accuracy with confusion matrix to show it the accuracy of classification poisonous or edible mushroom.

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Maternal mortality classification for health promotive in Dairi using machine learning approach

Cited by 6 publications

References 4 publications

On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature

On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature

On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature

Accuracy of classification poisonous or edible of mushroom using naïve bayes and k-nearest neighbors

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