Diagnosing Cancer Using IOT and Machine Learning Methods

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 9896490 - 9
• Main Authors: Maray, Mohammed, Alghamdi, Mohammed, Alazzam, Malik Bader
• Format: Journal Article
• Language: English
• Published: United States Hindawi 28-05-2022; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Algorithms; Analysis; Artificial intelligence; Breast cancer; Cancer; Classification; Data mining; Datasets; Decision trees; Deep learning; Disease; Feature selection; Gene expression; Learning algorithms; Libraries; Machine learning; Methods; Patients; Proteins; Size reduction; Support vector machines; Technology application; Womens health
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/9896490

Breast cancer affects one in every eight women and is the most common cancer. Aim. To diagnose breast cancer, a potentially fatal condition, using microarray technology, large datasets can now be used. Methods. This study used machine learning algorithms and IOT to classify microarray data. They were created from two sets of data: one with 1919 protein types and one with 24481 protein types for 97 people, 46 of whom had a recurring disease and 51 of whom did not. The apps were written in Python. Each classification algorithm was applied to the data separately, without any feature elimination or size reduction. Second, two alternative feature reduction approaches were compared to the first case. In this case, machine learning techniques like Adaboost and Gradient Boosting Machine are used. Results. Before applying any feature reduction techniques, the logistic regression method produced the best results (90.23%), while the Random Forest method produced good results (67.22%). In the first data, SVM had the highest accuracy rate of 99.23% in both approaches, while in the second data, SVM had the highest rate of 87.87% in RLR and 88.82% in LTE. Deep learning was also done with MLP. The relationship between depth and classification accuracy was studied using it at various depths. After a while, the accuracy rate declined as the number of layers increased. The maximum accuracy rate in the first data was 97.69%, while it was 68.72% in the second. As a result, adding layers to deep learning does not improve classification accuracy.