A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models

Khorshid, Shler Farhad; Abdulazeez, Adnan Mohsin; Sallow, Amira B.

doi:10.9734/ajrcos/2021/v8i430209

Cited by 13 publications

(5 citation statements)

References 48 publications

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“…e K-nearest neighbor algorithm is one of the simplest supervised learning models [9]. Its classification method does not require training time.…”

Section: K-nearest Neighborsmentioning

confidence: 99%

“…To address this public health issue, the study examines the performance of six machine learning algorithms for data classification: decision tree, random forest, K-nearest neighbors, support vector machines, artificial neural networks, and logistic regression [7]. e purpose of this paper is to discuss the accuracy and efficiency of predicting the occurrence of breast cancer in individuals based on input variables [8,9], which can be used as a diagnostic aid by the medical community. etic of breast cancer represents a quick and efficient solution that organizes patients so that more targeted measures can be taken, easing the doctors' work.…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning Models to the Detection of Breast Cancer

Binsaif

2022

Mobile Information Systems

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This work aims to build a binary breast cancer classifier algorithm based on the blood test and anthropometric data (age, body mass index, glucose, insulin, homeostasis model assessment, leptin, adiponectin, resistin, and monocyte chemotactic protein-1) of 116 subjects. For this study, a performance comparison of the following machine learning models was performed: decision tree, random forest, K-nearest neighbors, artificial neural networks, vector machines of support, and logistical regression. The methodologies used in the data were as follows: k‐fold cross‐validation (k = 10); splitting data into 80% training and 20% testing. For the first, the mean of accuracy and sensitivity were evaluated in the second, values of accuracy, sensitivity, specificity, and area under some tests. In addition, most mammograms are performed on benign tumors. With this, it is clear that these exams can use other tools to assist in decision-making, and machine learning can offer great utility and good cost/benefit in the diagnostic process of breast cancer. Many research papers for breast cancer biomarkers have been reported over the years. The present work will analyze the potential quantitative variables: age, receiver operating characteristic curve. Furthermore, the p value, Pearson correlation coefficient, and, depending on the input variable, the test only with variables with a significance threshold of 5% are computed from the normal distribution assessment (calculated from Kolmogorov–Smirnov test (KS test)) which were as follows: glucose, insulin, resistin, and homeostasis assessment model. As the best final classifier, the random forest was used in the training/test method and with nine variables, with 83.3% accuracy, 100% sensitivity, 64% specificity, and 0.881 of area under the curve.

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“…e K-nearest neighbor algorithm is one of the simplest supervised learning models [9]. Its classification method does not require training time.…”

Section: K-nearest Neighborsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Models to the Detection of Breast Cancer

Binsaif

2022

Mobile Information Systems

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“…Classification is the theoretical process of identifying individual components of an image or even the entire image. Image categorization is a critical and widely used branch of image processing that involves classifying images into a set of predefined categories using only representative samples from each category [44,45]. There are two types of image classification; an uncontrolled image classification exists [46].…”

Section: Classificationmentioning

confidence: 99%

Leukemia Diagnosis using Machine Learning Classifiers Based on Correlation Attribute Eval Feature Selection

Armya

Abdulazeez

Sallow

et al. 2021

AJRCoS

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Leukemia refers to a disease that affects the white blood cells (WBC) in the bone marrow and/or blood. Blood cell disorders are often detected in advanced stages as the number of cancer cells is much higher than the number of normal blood cells. Identifying malignant cells is critical for diagnosing leukemia and determining its progression. This paper used machine learning with classifiers to detect leukemia types as a result, it can save both patients and physicians time and money. The primary objective of this paper is to determine the most effective methods for leukemia detection. The WEKA application was used to evaluate and analyze five classifiers (J48, KNN, SVM, Random Forest, and Naïve Bayes classifiers). The results were respectively as follows: 83.33%, 87.5%, 95.83%, 88.88%, and 98.61%, with the Naïve Bayes classifier achieving the highest accuracy; however, accuracy varies according to the shape and size of the sample and the algorithm used to classify the leukemia types.

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“…Breast cancer, one of the most common diseases affecting women, costs a large number of lives each year. Although cancer may be treated and cured if detected early, many individuals do not obtain a diagnosis until it is too late [3,4]. Breast cancer starts when abnormalities in the cells become evident, and it can spread to nearby locations [5].…”

Section: Introductionmentioning

confidence: 99%

Cancer Unveiled: A Deep Dive Into Breast Tumor Detection Using Cutting-Edge Deep Learning Models

Arshad,

Masood,

Mahmood

et al. 2023

IEEE Access

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About 1.5 million women are diagnosed with breast cancer every year, making it the most frequent disease among women. In Pakistan, one woman in every nine has a lifetime chance of being diagnosed with breast cancer, making it the country with the highest incidence rate of breast cancer in Asia. The mortality rate from breast cancer in Pakistan was 22.7% in 2020. A lack of resources, such as competent pathologists, causes a delay in diagnosis and inadequate therapy planning, all of which contribute to a dismal survival rate. End-to-end solutions that may be implemented into computer-aided diagnostic (CAD) systems have been developed by medical professionals and researchers using domain-specific artificial intelligence (AI) technologies, most notably deep learning models, to address this critical issue. By increasing the amount of work for pathologists, these AI models may help in breast cancer detection and diagnosis. The goal of this research was to compare and contrast the effectiveness of many recent convolutional neural network (CNN) designs. Five pre-trained and fine-tuned deep CNN architectures, InceptionV3, ResNet152V2, MobileNetV2, VGG-16, and DenseNet-121, are tested to determine the best-performing model. The goal is to discover which models are preferable in terms of accuracy and effectiveness. Notably, the pre-trained InceptionV3 model outperforms the basic CNN model by 9%, with a high accuracy level of 94%. ResNet152V2 got 95% accuracy, and MobileNetV2 got 97% accuracy. The VGG-16 model outperforms the competition with a remarkable 98% accuracy rate. Following suit, the DenseNet-121 model achieves a remarkable 99% accuracy. These findings highlight the utility of deep learning models in the diagnosis of breast cancer as well as the range of model precision.

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A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models

Cited by 13 publications

References 48 publications

Application of Machine Learning Models to the Detection of Breast Cancer

Application of Machine Learning Models to the Detection of Breast Cancer

Leukemia Diagnosis using Machine Learning Classifiers Based on Correlation Attribute Eval Feature Selection

Cancer Unveiled: A Deep Dive Into Breast Tumor Detection Using Cutting-Edge Deep Learning Models

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