Enhancing Medical Diagnostics with Machine Learning: A Study on Ensemble Methods and Transfer Learning
This paper explores the use of machine learning (ML) in medicine, emphasizing how important it is to enhance patient outcomes and diagnostic precision. As medical data grows in complexity and volume, advanced ML techniques are increasingly necessary. The research focuses on leveraging Convolutional...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
|
| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02022.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This paper explores the use of machine learning (ML) in medicine, emphasizing how important it is to enhance patient outcomes and diagnostic precision. As medical data grows in complexity and volume, advanced ML techniques are increasingly necessary. The research focuses on leveraging Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Ensemble Methods, and Transfer Learning to enhance medical diagnostics. Specifically, these techniques are applied to large-scale datasets, to address tasks like disease detection, patient outcome prediction, and managing uncertainty in medical data. According to the study, CNNs performs substantially better when handling uncertainty when using the U-Multiclass technique, as seen by the largest Area Under the Curve (AUC) for Cardiomegaly detection. When it comes to diabetes prediction, Ensemble Methods outperform other approaches, and Transfer Learning works well for modifying trained models for use in novel medical applications. The research holds practical value since it can improve patient care and productivity within the healthcare industry. By integrating these ML techniques, the study contributes valuable insights into improving diagnostic processes and optimizing patient outcomes. |
|---|---|
| ISSN: | 2271-2097 |