Comparative studies on breast cancer classifications with k-fold cross validations using machine learning techniques

Nematzadeh, Zahra; Ibrahim, Roliana; Selamat, Ali

doi:10.1109/ascc.2015.7244654

Cited by 64 publications

(33 citation statements)

References 11 publications

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“…One group is used as the test set and the others as the training set. Firstly, the training set is used to train the model, and the model is verified via the test set [51, 52]. The common CV methods are the Hold-Out Method, K-fold Cross Validation (K-CV) and Leave-One-Out Cross Validation (LOO-CV).…”

Section: Methodsmentioning

confidence: 99%

Regional level influenza study based on Twitter and machine learning method

Xue

Bai

et al. 2019

PLoS ONE

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The significance of flu prediction is that the appropriate preventive and control measures can be taken by relevant departments after assessing predicted data; thus, morbidity and mortality can be reduced. In this paper, three flu prediction models, based on twitter and US Centers for Disease Control’s (CDC’s) Influenza-Like Illness (ILI) data, are proposed (models 1-3) to verify the factors that affect the spread of the flu. In this work, an Improved Particle Swarm Optimization algorithm to optimize the parameters of Support Vector Regression (IPSO-SVR) was proposed. The IPSO-SVR was trained by the independent and dependent variables of the three models (models 1-3) as input and output. The trained IPSO-SVR method was used to predict the regional unweighted percentage ILI (%ILI) events in the US. The prediction results of each model are analyzed and compared. The results show that the IPSO-SVR method (model 3) demonstrates excellent performance in real-time prediction of ILIs, and further highlights the benefits of using real-time twitter data, thus providing an effective means for the prevention and control of flu.

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Section: Methodsmentioning

confidence: 99%

Regional level influenza study based on Twitter and machine learning method

Xue

Bai

et al. 2019

PLoS ONE

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“…In [10], the dataset was taken from Iranian centre of breast cancer and compared decision tree, support vector machine and artificial neural network. Support vector machine was proven to be the best followed by an ANN and then the DT classification model.In [11], two datasets were taken for performing comparison among different machine learning models. The datasets were WPBC(Wisconsin Prognostic Breast Cancer) and Wisconsin breast cancer dataset.…”

Section: Literature Surveymentioning

confidence: 99%

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

Gupta*¹,

Gill²

2020

IJITEE

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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.

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“…Technologies including big data, Internet of Things (IoT), cloud and fog computing [1] have gained significance due to their available abilities to provide diverse services based on latency-sensitive or realtime applications [2]- [4]. Since the manual processing has not been effective, the use of the Artificial Intelligence (AI) in healthcare [5] has become prominent for monitoring, prognosis and diagnosis purposes [6]- [8]. Regarding the continuous COronaVIrus Disease 2019 (COVID-19) [9] pandemic growth across the world, several researchers are attempting to find solutions for exploring accurately the infected persons and isolate them to reduce the pandemic spread.…”

Section: Introductionmentioning

confidence: 99%

Hybrid-based Framework for COVID-19 Prediction via Federated Machine Learning Models

Kallel¹,

Rekik²,

Khemakhem³

2021

Preprint

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<div>The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. Therefore, a smart monitoring system that detects and tracks the suspected COVID-19 infected persons may improve the clinicians decision-making and consequently limit the pandemic spread. This paper entails a new framework integrating the Machine Learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, Lung UltraSound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a Service (federated-MLaaS); (i) the distributed batch-MLaaS, which is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream-MLaaS installed into a hybrid fog/cloud environment for a short-term decision-making. Stream-MLaaS use a shared federated prediction model stored into the cloud; whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream-ML algorithms from the Python’s libraries.</div>

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Comparative studies on breast cancer classifications with k-fold cross validations using machine learning techniques

Cited by 64 publications

References 11 publications

Regional level influenza study based on Twitter and machine learning method

Regional level influenza study based on Twitter and machine learning method

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

Hybrid-based Framework for COVID-19 Prediction via Federated Machine Learning Models

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