Supervised and Unsupervised Machine Learning for Cancer Classification: Recent Development
| Autor: | Muhammad Akmal Remli, Mohd Saberi Mohamad, Nor Bakiah Abd Warif, Noor Azida Sahabudin, Aina Umairah Mazlan, Nor Syahidatul Nadiah Ismail |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Cancer classification
business.industry Process (engineering) Computer science Machine learning computer.software_genre Field (computer science) ComputingMethodologies_PATTERNRECOGNITION Health care Optimization methods Unsupervised learning Artificial intelligence Healthcare data business computer Test data |
| Zdroj: | 2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS). |
| Popis: | This is models with the ability to detect and classify cancer is important in the industrial of healthcare. The most difficult aspect for such model is the classification of cancer, which can be addressed using machine learning methods. The methods are used to improve classification accuracy between system output and test data. The classification process becomes more difficult due to vast data information. This paper presents an overview on current development of cancer classification techniques using machine learning methods, which have received increasing attention within the area of healthcare. This review will mainly focus on the development of machine learning methods for classification of cancer diseases. Recently, there are various researchers proposed different kinds of methods for cancer classification. The results show that the successful of cancer classification is dependent on the machine learning models. Besides, various types of healthcare data used in the experiments would also be discussed in this paper. The development of many optimization methods for cancer classification has brought a lot of improvement in the healthcare field. There is demand for further improvements in optimization methods to develop better machine learning models for cancer classification. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|