Predicting Fetal Birthweight from High Dimensional Data using Advanced Machine Learning

• URL: http://arxiv.org/abs/2502.14270v1
• Date: Thu, 20 Feb 2025 05:17:39 GMT
• Title: Predicting Fetal Birthweight from High Dimensional Data using Advanced Machine Learning
• Authors: Nachiket Kapure, Harsh Joshi, Rajeshwari Mistri, Parul Kumari, Manasi Mali, Seema Purohit, Neha Sharma, Mrityunjoy Panday, Chittaranjan S. Yajnik,
• Abstract summary: Birth weight serves as a fundamental indicator of neonatal health, closely linked to medical interventions and long-term developmental risks. Traditional predictive models, often constrained by limited feature selection and incomplete datasets, struggle to achieve overlooking complex maternal and fetal interactions. This research explores machine learning to address these limitations, utilizing a structured methodology that integrates advanced imputation strategies.
• Score: 1.489994236178479
• License:
• Abstract: Birth weight serves as a fundamental indicator of neonatal health, closely linked to both early medical interventions and long-term developmental risks. Traditional predictive models, often constrained by limited feature selection and incomplete datasets, struggle to achieve overlooking complex maternal and fetal interactions in diverse clinical settings. This research explores machine learning to address these limitations, utilizing a structured methodology that integrates advanced imputation strategies, supervised feature selection techniques, and predictive modeling. Given the constraints of the dataset, the research strengthens the role of data preprocessing in improving the model performance. Among the various methodologies explored, tree-based feature selection methods demonstrated superior capability in identifying the most relevant predictors, while ensemble-based regression models proved highly effective in capturing non-linear relationships and complex maternal-fetal interactions within the data. Beyond model performance, the study highlights the clinical significance of key physiological determinants, offering insights into maternal and fetal health factors that influence birth weight, offering insights that extend over statistical modeling. By bridging computational intelligence with perinatal research, this work underscores the transformative role of machine learning in enhancing predictive accuracy, refining risk assessment and informing data-driven decision-making in maternal and neonatal care. Keywords: Birth weight prediction, maternal-fetal health, MICE, BART, Gradient Boosting, neonatal outcomes, Clinipredictive.

Related papers

• Swin fMRI Transformer Predicts Early Neurodevelopmental Outcomes from Neonatal fMRI [0.20482269513546453]
  Accurately predicting developmental outcomes during this time is crucial for identifying delays and enabling timely interventions. This study introduces the SwiFT (Swin 4D fMRI Transformer) model, designed to predict outcomes using neonatal fMRI. Our analysis shows that SwiFT significantly outperforms baseline models in predicting cognitive, motor, and language outcomes.
  arXiv  Detail & Related papers  (2024-11-25T12:20:07Z)
• Advancements In Heart Disease Prediction: A Machine Learning Approach For Early Detection And Risk Assessment [0.0]
  This paper comprehends, assess, and analyze the role, relevance, and efficiency of machine learning models in predicting heart disease risks using clinical data. The Support Vector Machine (SVM) demonstrates the highest accuracy at 91.51%, confirming its superiority among the evaluated models in terms of predictive capability.
  arXiv  Detail & Related papers  (2024-10-16T22:32:19Z)
• Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes [17.457683367235536]
  We identify and study the most important risk factors for four types of pregnancy complications: severe maternal morbidity, shoulder dystocia, preterm preeclampsia, and antepartum stillbirth. We use an Explainable Boosting Machine (EBM), a high-accuracy glass-box learning method, for prediction and identification of important risk factors.
  arXiv  Detail & Related papers  (2023-10-16T09:17:10Z)
• Unveiling the Unborn: Advancing Fetal Health Classification through Machine Learning [0.0]
  This research paper presents a novel machine-learning approach for fetal health classification. The proposed model achieves an impressive accuracy of 98.31% on a test set. By incorporating multiple data points, our model offers a more holistic and reliable evaluation.
  arXiv  Detail & Related papers  (2023-09-30T22:02:51Z)
• Copy Number Variation Informs fMRI-based Prediction of Autism Spectrum Disorder [9.544191399458954]
  We develop a more integrative model for combining genetic, demographic, and neuroimaging data. Inspired by the influence of genotype on phenotype, we propose using an attention-based approach. We evaluate the proposed approach on ASD classification and severity prediction tasks, using a sex-balanced dataset of 228 ASD.
  arXiv  Detail & Related papers  (2023-08-08T19:53:43Z)
• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• Predicting Adverse Neonatal Outcomes for Preterm Neonates with Multi-Task Learning [51.487856868285995]
  We first analyze the correlations between three adverse neonatal outcomes and then formulate the diagnosis of multiple neonatal outcomes as a multi-task learning (MTL) problem. In particular, the MTL framework contains shared hidden layers and multiple task-specific branches.
  arXiv  Detail & Related papers  (2023-03-28T00:44:06Z)
• NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants [73.85768093666582]
  We propose an explainable geometric deep network dubbed NeuroExplainer. NeuroExplainer is used to uncover altered infant cortical development patterns associated with preterm birth.
  arXiv  Detail & Related papers  (2023-01-01T12:48:12Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels [93.58854458951431]
  We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches. We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning.
  arXiv  Detail & Related papers  (2020-09-16T15:16:03Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.