Abstract
Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129.
Highlights
In recent decades, breast cancer has been a predominant cause of mortality amongst women [1,2]
The authors considered multiple classification models that include K-nearest neighbors [61], Naïve Bayes [62], Perceptron network [63], AdaBoost [64], and Support Vector Machine [65]. Compared all these classification models with the Extreme Learning Machine (ELM) on the standalone environment, and later, the ELM model was deployed on the cloud environment
This section contains the results that were collected from both the standalone and cloud environments, and the results were compared to visualize the performance as we shifted from the standalone environment to the cloud environment
Summary
Breast cancer has been a predominant cause of mortality amongst women [1,2]. Cloud computing services can be used to monitor patients, elderly people, and those with disabilities in remote or inaccessible villages and towns in many underdeveloped countries, where medical facilities and expertise are not readily available [10] In these areas, women with breast cancer are often left undiagnosed, and it is too late when they reach doctors available in larger cities. Doctors can use cloud computing to diagnose patients who cannot reach them due to a lack of financial resources They can use cloud computing for guidance through telehealth [11] and telemedicine [12], which includes the transmission of various medical data, such as high-resolution biomedical photographs and patient video recordings from remote areas to other geographic locations, where specialist physicians and large hospitals are situated. The remainder of this paper is organized a follows: In Section 2, related work is presented; in Section 3, a description of the methodology used in this work is provided; in Section 4, we discuss the setup of the experimental environment; in Section 5, we discuss the various results obtained in this study; and, in Section 6, we discuss the implications of the results as well as the conclusions and future work
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