Summary of Unified Dimensionality Reduction Techniques in Chronic Liver Disease Detection, by Anand Karna et al.
Unified dimensionality reduction techniques in chronic liver disease detection
by Anand Karna, Naina Khan, Rahul Rauniyar, Prashant Giridhar Shambharkar
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study investigates machine learning algorithms for predicting chronic liver disease using the Indian Liver Patient Dataset (ILPD). Researchers explore various feature extraction and dimensionality reduction techniques, such as Linear Discriminant Analysis (LDA), Factor Analysis (FA), t-distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). These methods are used to improve prediction accuracy, which is evaluated using classification algorithms like Multi-layer Perceptron, Random Forest, K-nearest neighbours, and Logistic Regression. The study finds that customized feature extraction and dimensionality reduction methods can significantly enhance predictive models for chronic liver disease patients. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies machine learning methods for detecting chronic liver disease. They use a special dataset with medical records of 583 people to test different algorithms. The goal is to find the best way to convert complex data into something easier to understand and improve predictions. The study finds that some algorithms, like Random Forest, work really well, achieving an accuracy of almost 99%. This research provides new insights on how to choose and use special techniques for extracting features and reducing dimensions, which can help create better predictive models for liver disease patients. |
Keywords
» Artificial intelligence » Classification » Dimensionality reduction » Embedding » Feature extraction » Logistic regression » Machine learning » Random forest » Umap
