article · Frontiers in Artificial Intelligence
Maternal health is an urgent public health concern, particularly where maternal mortality rates remain high in developing countries. To address this, a deep hybrid artificial intelligence model was developed to classify maternal health risks during pregnancy. The system integrates the capabilities of artificial neural networks and random forest algorithms. A maximum probability voting technique selects the output with the highest confidence between the two methods. The model relies on routine physiological indicators: age, systolic and diastolic blood pressure, blood sugar, body temperature, and heart rate. Using a testing split representing 25 per cent of the dataset, the hybrid model achieved 95 per cent accuracy, 97 per cent precision, 97 per cent recall, and an F1 score of 0.97, demonstrating high accuracy in categorising pregnancy risks from standard medical metrics.
Maternal mortality remains persistently high in developing countries, making early and accurate identification of pregnancy complications vital. By leveraging routine clinical measurements such as blood pressure and heart rate, this automated risk-classification approach can help healthcare systems identify high-risk pregnancies more reliably, supporting timely interventions to protect both mothers and infants.
The technology could eventually support software applications or clinical decision-support tools used by healthcare practitioners to screen pregnant patients. At present, it represents early-stage research tested on a retrospective dataset. Commercial or clinical deployment remains distant, as the abstract notes that validating generalisability across diverse populations, incorporating unstructured medical data, and evaluating feasibility in clinical settings are still required.
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Introduction: Maternal health is a critical aspect of public health that affects the wellbeing of both mothers and infants. Despite medical advancements, maternal mortality rates remain high, particularly in developing countries. AI-based models provide new ways to analyze and interpret medical data, which can ultimately improve maternal and fetal health outcomes. Methods: This study proposes a deep hybrid model for maternal health risk classification in pregnancy, which utilizes the strengths of artificial neural networks (ANN) and random forest (RF) algorithms. The proposed model combines the two algorithms to improve the accuracy and efficiency of risk classification in pregnant women. The dataset used in this study consists of features such as age, systolic and diastolic blood pressure, blood sugar, body temperature, and heart rate. The dataset is divided into training and testing sets, with 75% of the data used for training and 25% used for testing. The output of the ANN and RF classifier is considered, and a maximum probability voting system selects the output with the highest probability as the most correct. Results: Performance is evaluated using various metrics, such as accuracy, precision, recall, and F1 score. Results showed that the proposed model achieves 95% accuracy, 97% precision, 97% recall, and an F1 score of 0.97 on the testing dataset. Discussion: The deep hybrid model proposed in this study has the potential to improve the accuracy and efficiency of maternal health risk classification in pregnancy, leading to better health outcomes for pregnant women and their babies. Future research could explore the generalizability of this model to other populations, incorporate unstructured medical data, and evaluate its feasibility for clinical use.
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DOI: 10.3389/frai.2023.1213436
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