Deep hybrid model for maternal health risk classification in pregnancy: synergy of ANN and random forest

Abstract

IntroductionMaternal 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.MethodsThis 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.ResultsPerformance 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.DiscussionThe 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.

Empowering Women’s Health: A Global Perspective on Artificial Intelligence and Robotics

“…Moreover, a substantial percentage of pregnant women are open to integrating AI into maternal healthcare, although acceptance levels are influenced by their education and familiarity with AI technology. This shift suggests a promising role of AI in advancing maternal health care [4].…”

“…Utilizing vast datasets, AI models are capable of identifying individuals at high risk of adverse outcomes, assisting healthcare providers in making timely and precise decisions. Recent studies have underscored the potential of AI in predicting complications such as preterm birth, preeclampsia, and gestational diabetes using various health indicators and AI models [4]. Moreover, a substantial percentage of pregnant women are open to integrating AI into maternal healthcare, although acceptance levels are influenced by their education and familiarity with AI technology.…”

Fetal monitoring technologies for the detection of intrapartum hypoxia - challenges and opportunities

“…Additionally, during advanced labour stages, a more comprehensive analysis of CTG traces is recommended, including blinded reviews, universal cord gas collection, and a focus on identifying features linked with genuine pathology [163]. Incorporating clinical variables such as gestational age and maternal temperature into classification criteria can enhance precision in CTG classifications [164]. Moreover, extensive research efforts are vital for understanding the long-term consequences of CTG classifications and evaluating the impact of diverse technologies and equipment in different healthcare settings [164].…”

“…Incorporating clinical variables such as gestational age and maternal temperature into classification criteria can enhance precision in CTG classifications [164]. Moreover, extensive research efforts are vital for understanding the long-term consequences of CTG classifications and evaluating the impact of diverse technologies and equipment in different healthcare settings [164]. Lastly, standardized CTG datasets can facilitate both research and clinical practice, ultimately contributing to enhanced accuracy and reliability in fetal monitoring during labour [19,162].…”

Artificial Intelligence Trends and Tools for Improving Women’s Health

“…This makes them an invaluable tool for physicians, especially in critical care settings like surgical operations. AI applications in healthcare fall into two categories: Natural language processing techniques that pull information from unstructured sources like clinical notes and medical literature, and ML approaches that use structured data analysis to predict illness prognosis, especially in light of genetic effects [6,7].…”