scholarly journals Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 241
Author(s):  
Vivek Lahoura ◽  
Harpreet Singh ◽  
Ashutosh Aggarwal ◽  
Bhisham Sharma ◽  
Mazin Abed Mohammed ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cloud Computing ◽  
Extreme Learning Machine ◽  
Feature Selection Method ◽  
Breast Cancer Diagnosis ◽  
Disease Diagnosis ◽  
Classification Problems ◽  
Risk Of Death ◽  
Learning Machine

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.

scholarly journals An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

2020 ◽  
Vol 10 (5) ◽  
pp. 1827 ◽  
Author(s):  
Rodrigo Olivares ◽  
Roberto Munoz ◽  
Ricardo Soto ◽  
Broderick Crawford ◽  
Diego Cárdenas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Extreme Learning Machine ◽  
Neurodegenerative Disorder ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Classification Problems ◽  
Learning Machine

During the last years, highly-recognized computational intelligence techniques have been proposed to treat classification problems. These automatic learning approaches lead to the most recent researches because they exhibit outstanding results. Nevertheless, to achieve this performance, artificial learning methods firstly require fine tuning of their parameters and then they need to work with the best-generated model. This process usually needs an expert user for supervising the algorithm’s performance. In this paper, we propose an optimized Extreme Learning Machine by using the Bat Algorithm, which boosts the training phase of the machine learning method to increase the accuracy, and decreasing or keeping the loss in the learning phase. To evaluate our proposal, we use the Parkinson’s Disease audio dataset taken from UCI Machine Learning Repository. Parkinson’s disease is a neurodegenerative disorder that affects over 10 million people. Although its diagnosis is through motor symptoms, it is possible to evidence the disorder through variations in the speech using machine learning techniques. Results suggest that using the bio-inspired optimization algorithm for adjusting the parameters of the Extreme Learning Machine is a real alternative for improving its performance. During the validation phase, the classification process for Parkinson’s Disease achieves a maximum accuracy of 96.74% and a minimum loss of 3.27%.

scholarly journals Machine Learning Techniques and Extreme Learning Machine for Early Breast Cancer Prediction

2020 ◽  
Vol 9 (4) ◽  
pp. 163-167
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Extreme Learning Machine ◽  
Machine Learning Techniques ◽  
Random Forest Classification ◽  
Decision Tree Classification ◽  
Medical Tests ◽  
Forest Classification ◽  
Learning Techniques ◽  
Learning Machine

Breast Cancer is one of the most deadly disease and most of the women are infected by this vital disease in many parts of the world. Medical tests conducted in hospitals for determining the disease are very much expensive as well as timeconsuming. The problem can be resolved by diagnosing the problem in early spam of time and by providing results with more accuracy.In this paper, different machine learning and neural network algorithmhave been studied and compared to predict cancer in early stages so that life can be saved. The dataset available publically for Breast Cancer has been used. Different algorithms compared includeSupport Vector Machine Classification (SVM), K-Nearest NeighborClassification (KNN), Decision tree Classification (DT), Random Forest Classification (RF) and Extreme Learning Machine (ELM).All are compared on the basis of Accuracy and processing time are considered as the parameters for comparing analysis. The results reveal that extreme learning machine comes to be the better algorithm.

scholarly journals An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Derya Avci ◽  
Akif Dogantekin
Keyword(s):  
Genetic Algorithm ◽  
Parkinson Disease ◽  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Disease Diagnosis ◽  
Major Public Health Problem ◽  
Diagnosis System ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Neurons

Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%.

Sign in / Sign up

Export Citation Format

Share Document