It is all about deep learning techniques
- 1. SEMINAR PRESENTATION BY BADMUS TEMITOPE MARY FPS/CSC/20/65059 DEEP LEARNING TECHNIQUES FOR HANDLING IMBALANCED DATA IN HEALTH SECTOR A SEMINAR PRESENTED TO THE DEPARTMENT OF COMPUTER SCIENCE, FACULTY OF PHYSICAL SCIENCE, AMBROSE ALLI UNIVERSITY, EKPOMA, EDO STATE, NIGERIA. SUPERVISOR: MR O.H ONYIJEN
- 2. INTRODUCTION • Deep learning a subset of machine learning that utilize neutral network with multiple layers has shown promise in handling complex and large scale data • Deep learning is a subset of machine learning in artificial intelligence that involves neutral network with many layers. These neutral network are designed to simulate the human brains structure and function allowing them to learn and make decisions for large amount of data
- 3. Deep learning • Deep learning is a subset of machine learning, which in turn is a branch of artificial intelligence (AI). It focuses on algorithms inspired by the structure and function of the brain, specifically neural networks. Deep learning models are designed to recognize patterns, make decisions, and predict outcomes by analyzing vast amounts of data. • Types of Deep Learning Architectures: • Convolutional Neural Networks (CNNs): Primarily used in image processing and computer vision tasks. • Recurrent Neural Networks (RNNs): Used for sequential data, such as language or time series data, where context and order are important. • Generative Adversarial Networks (GANs): Involve two networks (a generator and a discriminator) competing against each other, commonly used in generating realistic images or videos.
- 4. IMBALANCED DATA • Imbalanced data occurs when the classes within a dataset are not represented equally, often presenting a significant difference in the frequency of instances among classes. This is common in areas like fraud detection, medical diagnostics, and anomaly detection, where the class of interest (e.g., fraudulent transactions or rare diseases) occurs much less frequently than the majority class (e.g., non-fraudulent transactions or healthy cases). • Techniques to Handle Imbalanced Data: • Resampling: Techniques like oversampling the minority class (e.g., Synthetic Minority Over-sampling Technique, or SMOTE) or undersampling the majority class can help balance the dataset. • Algorithmic Adjustments: Certain algorithms are specifically designed for imbalanced data, or can be tuned with class weights (e.g., in SVM or decision trees). • Ensemble Methods: Techniques like Random Forests and Boosting (e.g., AdaBoost, Gradient Boosting) can be effective as they combine multiple models, which may reduce class imbalance bias.
- 5. Deep learning techniques for health sector • Deep learning techniques are increasingly transforming the healthcare sector by enabling more accurate diagnostics, personalized treatment, and improved patient outcomes. Here’s an overview of key deep learning techniques and their applications in healthcare: • Convolutional Neural Networks (CNNS) :CNNs are widely used for analyzing medical images such as X-rays, CT scans, and MRIs. They help in detecting anomalies, tumors, and other pathologies. • Recurrent Neural Networks (RNNs): RNNs are used for predicting patient outcomes over time, analyzing vital signs, and monitoring chronic diseases. • Generative Adversarial Networks (GANs): Creating synthetic medical images for training data augmentation, especially in scenarios where data is limited.
- 6. Deep learning application in healthcare • Deep learning has become a transformative technology in healthcare, offering various applications that enhance diagnostics, treatment, and patient care. Here are some notable applications: • Medical Imaging: Radiology: Deep learning algorithms analyze medical images (X-rays, CT scans, MRIs) to detect anomalies like tumors or fractures. Models like convolutional neural networks (CNNs) have shown high accuracy in identifying conditions such as lung cancer and diabetic retinopathy. • Genomics: Deep learning models analyze genetic data to identify mutations and variations linked to specific diseases. This aids in personalized medicine, allowing tailored treatment plans based on genetic profiles.
- 7. Case study • Deep learning has the potential to transform healthcare by providing accurate, efficient and scalable solutions for diseases and diagnosis. The case study of diabetic retinopathy detection demonstrates the significant impact that deep learning can have in improving patient outcomes and reducing the burden on healthcare providers. Continued advancement in this field will further enhance the capabilities and application of deep learning techniques in the health sector. Handling imbalanced data in the health sector, especially in the context of deep learning is crucial for ensuring accurate and reliable predictions. One popular technique for dealing with imbalanced data is called synthetic minority over sampling techniques SMOTE
- 8. Prospect and Challenges • Prospect • Deep learning techniques can be combined with other AI methods (e.g., NLP for clinical notes) to provide comprehensive healthcare solutions that address both data imbalance and data richness. • By utilizing deep learning techniques to handle imbalanced data, models can achieve higher sensitivity for detecting rare diseases, leading to earlier and more accurate diagnoses. • Challenges • High-quality, annotated datasets are often scarce, particularly for rare conditions. This limits the ability to train models effectively, especially in the minority class. • If the data used to train deep learning models is biased (e.g., underrepresentation of certain demographics), the resulting models may also be biased, leading to disparities in healthcare outcomes.
- 9. CONCLUSION Deep learning techniques, including data resampling, cost-sensitive learning, and advanced architectures, effectively address class imbalance in healthcare, enhancing predictive performance for minority classes. • Adopting appropriate evaluation metrics beyond accuracy is crucial for accurately assessing model effectiveness, ultimately improving diagnostic accuracy and patient care outcomes.