Machine Learning Approaches for Inferring Liver Diseases and Detecting Blood Donors from Medical Diagnosis

• URL: http://arxiv.org/abs/2104.12055v1
• Date: Sun, 25 Apr 2021 04:10:19 GMT
• Title: Machine Learning Approaches for Inferring Liver Diseases and Detecting Blood Donors from Medical Diagnosis
• Authors: Fahad B. Mostafa and Md Easin Hasan
• Abstract summary: In this paper, multiple imputation by chained equations was applied to deal with missing data. To reveal significant findings, data visualizations were implemented. Our proposed ML-method showed better accuracy score (e.g. 98.23% for SVM)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For a medical diagnosis, health professionals use different kinds of pathological ways to make a decision for medical reports in terms of patients medical condition. In the modern era, because of the advantage of computers and technologies, one can collect data and visualize many hidden outcomes from them. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning data driven algorithms can be used to validate existing methods and help researchers to suggest potential new decisions. In this paper, multiple imputation by chained equations was applied to deal with missing data, and Principal Component Analysis to reduce the dimensionality. To reveal significant findings, data visualizations were implemented. We presented and compared many binary classifier machine learning algorithms (Artificial Neural Network, Random Forest, Support Vector Machine) which were used to classify blood donors and non-blood donors with hepatitis, fibrosis and cirrhosis diseases. From the data published in UCI-MLR [1], all mentioned techniques were applied to find one better method to classify blood donors and non-blood donors (hepatitis, fibrosis, and cirrhosis) that can help health professionals in a laboratory to make better decisions. Our proposed ML-method showed better accuracy score (e.g. 98.23% for SVM). Thus, it improved the quality of classification.

Related papers

• Gradient Boosting Decision Trees on Medical Diagnosis over Tabular Data [0.0]
  Ensemble methods offer a powerful alternative in terms of providing successful medical decision-making processes. In this study, we investigated the benefits of ensemble methods, especially the Gradient Boosting Decision Tree (GBDT) algorithms in medical classification tasks.
  arXiv  Detail & Related papers  (2024-09-25T17:13:05Z)
• Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method [0.0]
  This research paper focuses on Acute Lymphoblastic Leukemia (ALL), a form of blood cancer prevalent in children and teenagers. It proposes an automated detection approach using computer-aided diagnostic (CAD) models, leveraging deep learning techniques. The proposed method achieved an impressive 98.38% accuracy, outperforming other tested models.
  arXiv  Detail & Related papers  (2023-12-01T10:37:02Z)
• 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)
• AI Framework for Early Diagnosis of Coronary Artery Disease: An Integration of Borderline SMOTE, Autoencoders and Convolutional Neural Networks Approach [0.44998333629984877]
  We develop a methodology for balancing and augmenting data for more accurate prediction when the data is imbalanced and the sample size is small. The experimental results revealed that the average accuracy of our proposed method for CAD prediction was 95.36, and was higher than random forest (RF), decision tree (DT), support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN)
  arXiv  Detail & Related papers  (2023-08-29T14:33:38Z)
• Recent advancement in Disease Diagnostic using machine learning: Systematic survey of decades, comparisons, and challenges [0.0]
  Pattern recognition and machine learning in the biomedical area promise to increase the precision of disease detection and diagnosis. This review article examines machine-learning algorithms for detecting diseases, including hepatitis, diabetes, liver disease, dengue fever, and heart disease.
  arXiv  Detail & Related papers  (2023-07-31T16:35:35Z)
• 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)
• Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images [52.527733226555206]
  We investigate the use of four attribution methods to explain a multiple instance learning models. We study two datasets of acute myeloid leukemia with over 100 000 single cell images. We compare attribution maps with the annotations of a medical expert to see how the model's decision-making differs from the human standard.
  arXiv  Detail & Related papers  (2023-03-15T14:00:11Z)
• When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning Framework in Classification of Medical Images on Limited Data: A COVID-19 Case Study [77.34726150561087]
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. CNN has been widely utilized and verified in analyzing medical images.
  arXiv  Detail & Related papers  (2022-03-24T02:09:41Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• 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)
• Approaching Bio Cellular Classification for Malaria Infected Cells Using Machine Learning and then Deep Learning to compare & analyze K-Nearest Neighbours and Deep CNNs [0.0]
  Malaria is a deadly disease which claims the lives of hundreds of thousands of people every year. This paper examines different machine learning methods in the context of classifying the presence of malaria in cell images.
  arXiv  Detail & Related papers  (2020-05-22T23:02:36Z)

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.