A Survey on Data Mining Technologies for Decision Support System of Maternal Care Domain

Mehta, R. Vasanth Kumar; Bhatt, Nikita; Ganatra, Amit

doi:10.5120/ijca2016908965

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…The literature is scarce in this respect. We found a few decision-trees reports to forecast preterm birth, neonatal jaundice, neonatal infections and a reference paper for predicting neurological outcomes in full-term neonates with encephalopathy using decision trees (CART) and logistic regression [ 5 , 62 , 63 , 64 , 65 , 66 ]. In the latter, though, the focus was only on a specific age group (term infants) and a specific etiology causing the encephalopathy (HIE).…”

Section: Discussionmentioning

confidence: 99%

A Decision-Tree Approach to Assist in Forecasting the Outcomes of the Neonatal Brain Injury

Neamţu

Visa²,

Maniu

et al. 2021

IJERPH

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Neonatal brain injury or neonatal encephalopathy (NE) is a significant morbidity and mortality factor in preterm and full-term newborns. NE has an incidence in the range of 2.5 to 3.5 per 1000 live births carrying a considerable burden for neurological outcomes such as epilepsy, cerebral palsy, cognitive impairments, and hydrocephaly. Many scoring systems based on different risk factor combinations in regression models have been proposed to predict abnormal outcomes. Birthweight, gestational age, Apgar scores, pH, ultrasound and MRI biomarkers, seizures onset, EEG pattern, and seizure duration were the most referred predictors in the literature. Our study proposes a decision-tree approach based on clinical risk factors for abnormal outcomes in newborns with the neurological syndrome to assist in neonatal encephalopathy prognosis as a complementary tool to the acknowledged scoring systems. We retrospectively studied 188 newborns with associated encephalopathy and seizures in the perinatal period. Etiology and abnormal outcomes were assessed through correlations with the risk factors. We computed mean, median, odds ratios values for birth weight, gestational age, 1-min Apgar Score, 5-min Apgar score, seizures onset, and seizures duration monitoring, applying standard statistical methods first. Subsequently, CART (classification and regression trees) and cluster analysis were employed, further adjusting the medians. Out of 188 cases, 84 were associated to abnormal outcomes. The hierarchy on etiology frequencies was dominated by cerebrovascular impairments, metabolic anomalies, and infections. Both preterms and full-terms at risk were bundled in specific categories defined as high-risk 75–100%, intermediate risk 52.9%, and low risk 0–25% after CART algorithm implementation. Cluster analysis illustrated the median values, profiling at a glance the preterm model in high-risk groups and a full-term model in the inter-mediate-risk category. Our study illustrates that, in addition to standard statistics methodologies, decision-tree approaches could provide a first-step tool for the prognosis of the abnormal outcome in newborns with encephalopathy.

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Section: Discussionmentioning

confidence: 99%

A Decision-Tree Approach to Assist in Forecasting the Outcomes of the Neonatal Brain Injury

Neamţu

Visa²,

Maniu

et al. 2021

IJERPH

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“…Machine learning algorithms can be used to specify and predate high-risk prediction of the unknown level. This case decision tree is the most used technique for better accuracy and prediction rather than the regression model among all other algorithms in the health domain with less error (Mehta, Bhatt, & Ganatra, 2016). Data mining and machine learning are the best and fastestgrowing features of knowledge discovery in a dataset.…”

Section: Machine Learning In Medical Sciencementioning

confidence: 99%

Maternal HealthCare Using IoT-Based Integrated Medical Device: Bangladesh Perspective

Kashem,

Ahmed,

Mohammad

2023

Journal of Multidisciplinary Academic and Practice Studies

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Purpose: The main purpose of this study is to develop a low-cost integrated medical device. This device will help investigate the risk levels of maternal patients as well will reduce the cost involved in medical diagnosis for poor countries like Bangladesh, where maternal healthcare is a great concern. Methodology: We proposed and developed an integrated medical device that includes all possible sensors to collect raw data from maternal patients. As soon as the data is collected it will be sent to the cloud for processing. In the cloud, there are a set of algorithms (software) for processing raw data received from the device. After processing and analyzing our system will automatically identify the risk levels of those patients. The software is developed in open source code so that in the future it can be updated by researchers. Results: We developed the system and practically collected raw data from patients and uploaded those data to our cloud system. In the cloud, it was processed and the resultant data were presented in the form of graphs. From these graphs, the risk levels were identified. Limitations: The proposed system is developed for maternal patients only. This system needs to be authorized by the health regulatory authority. To make it cost-effective some expensive sensors were not used. Contribution: The main contribution of this study is to minimize the cost involved in maternal healthcare in poor countries like Bangladesh. This, in turn, controls the death of mother and child by improving maternal healthcare facilities.

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“…However, a universal and multi-label classification with Extreme Learning Machine (ELM) classification approach capable of performing the functions of the three aforementioned classifiers were proposed by [11] and [14], respectively. The survey conducted by [27], provided information about the association rule, classification and cluster analysis as useful tools in the identification and discovery of risk in maternal care. These tools are developed using a few underlying algorithms that have been used for mining maternal-related care, such as DT, NB, KNN, ANN, SVM, RF, Gaussian NB and so on [28][29][30].…”

Section: Classification Approaches For Medical Diagnosticmentioning

confidence: 99%

Optimality Assessments of Classifiers on Single and Multi-labelled Obstetrics Outcome Classification Problems

Udo¹,

Samuel²,

Funebi³

et al. 2021

IJACSA

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It is indisputable that clinicians cannot exactly state the outcome of pregnancies through conventional knowledge and methods even as the surge in human knowledge continues. Hence, several computational techniques have been adapted for precise pregnancy outcome (PO) prediction. Obstetric datasets for PO determination exist as single label learning (SLL), multi-label learning (MLL) and multi-target (MTP) problems. There is however no single classifier recommended to optimally satisfy the needs of all the classification types. This work therefore identifies six widely used PO classifiers and investigates their performances in all three classification categories; to find the best performing classifier. Obstetric dataset exposed to input rank analysis via Principal component Analysis, produced thirteen (13) significant features for the experiment. Accuracy, F1-measure and build/test time were used as evaluation metrics. Decision tree (DT) had an average accuracy and F1 score of 89.23% and 88.23% respectively, with 1.0 average rank. Under MLL configuration, average accuracy (91.71%) and F1 score (94.28%) were highest in the random forest (RF) which had a 1.0 average test time rank. Using MTP, DT had an average accuracy of 88.80% and average F1 score of 71.13%, the multi-layered perceptron (MLP) had the best time cost with an average rank value of 2.0. From the results, RF is most optimal in terms of accuracy and average rank value, while DT is the most efficient in terms of time cost. The comparative analysis of global averages of the six base classifiers shows that RF is the most optimal algorithm with an average accuracy of 87.3% given all three data setups in the study. MLP on the other hand had an unexpectedly high time cost, making it unsuitable for similar data classifications if time is the main criterion. It is recommended that the choice of the classifier should either be RF or DT depending on the application domain and whether or not time cost is a major consideration.

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A Survey on Data Mining Technologies for Decision Support System of Maternal Care Domain

Cited by 7 publications

References 5 publications

A Decision-Tree Approach to Assist in Forecasting the Outcomes of the Neonatal Brain Injury

A Decision-Tree Approach to Assist in Forecasting the Outcomes of the Neonatal Brain Injury

Maternal HealthCare Using IoT-Based Integrated Medical Device: Bangladesh Perspective

Optimality Assessments of Classifiers on Single and Multi-labelled Obstetrics Outcome Classification Problems

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