Particle Swarm Optimization Feature Selection for Breast Cancer Recurrence Prediction
| Autor: | Sapiah Binti Sakri, Zuhaira Muhammad Zain, Nur'Aini Abdul Rashid |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
recurrence General Computer Science K-nearest neighbor Computer science Feature extraction Decision tree Feature selection 02 engineering and technology Machine learning computer.software_genre REPTree Data modeling 03 medical and health sciences Naive Bayes classifier Breast cancer feature selection 0202 electrical engineering electronic engineering information engineering medicine naïve Bayes General Materials Science business.industry General Engineering Particle swarm optimization medicine.disease Statistical classification 030104 developmental biology 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business computer lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 6, Pp 29637-29647 (2018) |
| ISSN: | 2169-3536 |
| Popis: | Women who have recovered from breast cancer (BC) always fear its recurrence. The fact that they have endured the painstaking treatment makes recurrence their greatest fear. However, with current advancements in technology, early recurrence prediction can help patients receive treatment earlier. The availability of extensive data and advanced methods make accurate and fast prediction possible. This research aims to compare the accuracy of a few existing data mining algorithms in predicting BC recurrence. It embeds a particle swarm optimization as feature selection into three renowned classifiers, namely, naive Bayes, K-nearest neighbor, and fast decision tree learner, with the objective of increasing the accuracy level of the prediction model. |
| Databáze: | OpenAIRE |
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