Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data

• URL: http://arxiv.org/abs/2311.10962v1
• Date: Sat, 18 Nov 2023 04:01:46 GMT
• Title: Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data
• Authors: Binod Regmi and Chiranjibi Shah
• Abstract summary: We have analyzed the classification performance of various machine learning models for fetal health analysis. A TabNet model on a fetal health dataset provides a classification accuracy of 94.36%.
• Score: 1.3597551064547502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The persistent battle to decrease childhood mortality serves as a commonly employed benchmark for gauging advancements in the field of medicine. Globally, the under-5 mortality rate stands at approximately 5 million, with a significant portion of these deaths being avoidable. Given the significance of this problem, Machine learning-based techniques have emerged as a prominent tool for assessing fetal health. In this work, we have analyzed the classification performance of various machine learning models for fetal health analysis. Classification performance of various machine learning models, such as support vector machine (SVM), random forest(RF), and attentive interpretable tabular learning (TabNet) have been assessed on fetal health. Moreover, dimensionality reduction techniques, such as Principal component analysis (PCA) and Linear discriminant analysis (LDA) have been implemented to obtain better classification performance with less number of features. A TabNet model on a fetal health dataset provides a classification accuracy of 94.36%. In general, this technology empowers doctors and healthcare experts to achieve precise fetal health classification and identify the most influential features in the process.

Related papers

• 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)
• Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI [0.0]
  We evaluate and compare the classification accuracy, precision, recall, and F-1 scores of five different machine learning methods. XGBoost achieved the best model accuracy, which is 97%.
  arXiv  Detail & Related papers  (2024-04-06T17:23:21Z)
• A Saliency-based Clustering Framework for Identifying Aberrant Predictions [49.1574468325115]
  We introduce the concept of aberrant predictions, emphasizing that the nature of classification errors is as critical as their frequency. We propose a novel, efficient training methodology aimed at both reducing the misclassification rate and discerning aberrant predictions. We apply this methodology to the less-explored domain of veterinary radiology, where the stakes are high but have not been as extensively studied compared to human medicine.
  arXiv  Detail & Related papers  (2023-11-11T01:53:59Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• 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)
• An Improved Model Ensembled of Different Hyper-parameter Tuned Machine Learning Algorithms for Fetal Health Prediction [1.332560004325655]
  We propose a robust ensemble model called ensemble of tuned Support Vector Machine and ExtraTrees for predicting fetal health. Our proposed ETSE model outperformed the other models with 100% precision, 100% recall, 100% F1-score, and 99.66% accuracy.
  arXiv  Detail & Related papers  (2023-05-26T16:40:44Z)
• RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging [67.02500668641831]
  Deep learning models trained on noisy datasets are sensitive to the noise type and lead to less generalization on unseen samples. We propose a Robust Knowledge Distillation (RoS-KD) framework which mimics the notion of learning a topic from multiple sources to ensure deterrence in learning noisy information. RoS-KD learns a smooth, well-informed, and robust student manifold by distilling knowledge from multiple teachers trained on overlapping subsets of training data.
  arXiv  Detail & Related papers  (2022-10-15T22:32:20Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data [47.00510780034136]
  Class imbalance is a familiar issue in the field of disease prediction. In this paper, we propose Re-weighted Adversarial Graph Convolutional Network (RA-GCN) to enhance the performance of the graph-based classifier. We show the superiority of RA-GCN on synthetic and three publicly available medical datasets compared to the recent method.
  arXiv  Detail & Related papers  (2021-02-27T14:06:27Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)
• A survey of statistical learning techniques as applied to inexpensive pediatric Obstructive Sleep Apnea data [3.1373682691616787]
  obstructive sleep apnea affects an estimated 1-5% of elementary-school aged children. Swift diagnosis and treatment are critical to a child's growth and development, but the variability of symptoms and the complexity of the available data make this a challenge. We apply correlation networks, the Mapper algorithm from topological data analysis, and singular value decomposition in a process of exploratory data analysis.
  arXiv  Detail & Related papers  (2020-02-17T18:15:32Z)

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.