Summary of The Identification and Categorization Of Anemia Through Artificial Neural Networks: a Comparative Analysis Of Three Models, by Mohammed A. A. Elmaleeh
The Identification and Categorization of Anemia Through Artificial Neural Networks: A Comparative Analysis of Three Models
by Mohammed A. A. Elmaleeh
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 paper presents different neural network-based classifier algorithms for diagnosing and classifying Anemia. The study compares these classifiers with established models like Feed Forward Neural Network (FFNN), Elman network, and Non-linear Auto-Regressive Exogenous model (NARX). Experimental evaluations were conducted using data from clinical laboratory test results for 230 patients. The proposed neural network features nine inputs (age, gender, RBC, HGB, HCT, MCV, MCH, MCHC, WBCs) and one output. The simulation outcomes demonstrate that the suggested artificial neural network rapidly and accurately detects the presence of Anemia. This could lead to seamless integration into clinical laboratories for automatic report generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using special kinds of computer programs called neural networks to help doctors diagnose a type of blood disorder called Anemia. They tested different types of these programs against some established ones, and used data from 230 patients to see how well they worked. The new program has nine inputs like age, gender, and test results, and one output that shows whether someone has Anemia or not. The program is fast, accurate, and could be used in hospitals to help doctors make diagnoses. |
Keywords
* Artificial intelligence * Neural network
