Using Random Forest Algorithm for Breast Cancer Diagnosis

Dai, Bin; Chen, Rung-Ching; Zhu, Shengqi; Zhang, Weiwei

doi:10.1109/is3c.2018.00119

Cited by 75 publications

(34 citation statements)

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“…ese might be small and stored inside the breast. Doctors should confirm the size of the tumor [12] at other stages of cancer. In zero stage, this disease remains in the tube of the breast.…”

Section: Stages Of Cancer Which Is Related To Mammary Glandsmentioning

confidence: 99%

“…e authors have also used random forest algorithm for breast cancer diagnosis [12], genetic algorithm-based ensemble approach [22], and Bayesian logistic regression [26] for breast cancer prediction. Breast mass classification and its diagnosis have been made using mammograms using ensemble convolution neural networks [27].…”

Section: Problem Statement Existing Researches Made Use Of Breast Cancer Categorization Of Graphical Content With Help Of Cnnmentioning

confidence: 99%

“…In 2018, Dai et al [12] have done some work to find cancer that is related to the breast. In this work, a random forest algorithm was adopted.…”

mentioning

confidence: 99%

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Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

Madhu

Kumar

2021

Mathematical Problems in Engineering

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There are many research studies in the field of breast cancer prediction, but it has been observed that the time taken for prediction needs to be reduced. The problem in the existing research is space consumption by graphical content. The proposed research is supposed to minimize the prediction time and space consumption. In this paper, research has focused on the study of existing breast cancer research and techniques and eliminating their limitation. It has been observed that when the number of datasets increases, every comparison makes a huge gap in size and comparison time. This research proposes a methodology for breast cancer prediction using an edge-based CNN (convolutional neural network) algorithm. The elimination of useless content from the graphical image before applying CNN has reduced the time consumption along with space consumption. The edge detection mechanism would retail only edges from the image sample in order to detect the pattern to predict breast cancer. The proposed work is supposed to implement the proposed methodology. A comparison of the proposed methodology and algorithm with the existing algorithm is made during simulation. The proposed work is found to be more efficient compared to the existing techniques used in breast cancer prediction. The utilization of proposed in the work area of medical science is supposed to enhance the capability in case of CNN at the time of decision-making. The proposed work is supposed to be more accurate compared to the existing works. It has been observed that the proposed work is fourteen to fifteen percent more accurate. It is taking 9/4 times less space and 1.0849004/0.178971 times less time compared to the general CNN model. Accuracy might vary as per size of the image and alteration performed in dataset of the image.

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Section: Stages Of Cancer Which Is Related To Mammary Glandsmentioning

confidence: 99%

Section: Problem Statement Existing Researches Made Use Of Breast Cancer Categorization Of Graphical Content With Help Of Cnnmentioning

confidence: 99%

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Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

Madhu

Kumar

2021

Mathematical Problems in Engineering

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“…One of the functionalities of the module is the clustering of patients based on the demographic and medical data by using the DNN [41]. Logistic regression [42], Random Forest [43] and DNN are used for the implementation of subsystem for the smart identification and assessment of patients who will not come to the appointment with the chosen physician or/and to expensive diagnostic examinations for which a patient needs to wait, sometimes even for several months. This system has enabled patients to make appointments in overlapping slots [44] during the COVID-19 pandemic.…”

Section: Pre-existing Modules From Mis Medisnetmentioning

confidence: 99%

Extensions and adaptations of existing medical information system in order to reduce social contacts during COVID-19 pandemic

Milenković

Janković

Rajkovic

2020

International Journal of Medical Informatics

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Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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“…Chari et al Classified diabetes using a random forest algorithm with feature selection [5]. Dai et al also conducted a survey using a random forest algorithm to diagnose breast cancer [6]. Iwendi et al used a RFA to predict patients' health exposed to Covid-19 [7].…”

Section: Introductionmentioning

confidence: 99%

Random Forest Algorithm to Investigate the Case of Acute Coronary Syndrome

Cynthia

Nazir

Syafria

2021

RESTI

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This paper explains the use of the Random Forest Algorithm to investigate the Case of Acute Coronary Syndrome (ACS). The objectives of this study are to review the evaluation of the use of data science techniques and machine learning algorithms in creating a model that can classify whether or not cases of acute coronary syndrome occur. The research method used in this study refers to the IBM Foundational Methodology for Data Science, include: i) inventorying dataset about ACS, ii) preprocessing for the data into four sub-processes, i.e. requirements, collection, understanding, and preparation, iii) determination of RFA, i.e. the "n" of the tree which will form a forest and forming trees from the random forest that has been created, and iv) determination of the model evaluation and result in analysis based on Python programming language. Based on the experiments that the learning have been conducted using a random forest machine-learning algorithm with an n-estimator value of 100 and each tree's depth (max depth) with a value of 4, learning scenarios of 70:30, 80:20, and 90:10 on 444 cases of acute coronary syndrome data. The results show that the 70:30 scenario model has the best results, with an accuracy value of 83.45%, a precision value of 85%, and a recall value of 92.4%. Conclusions obtained from the experiment results were evaluated with various statistical metrics (accuracy, precision, and recall) in each learning scenario on 444 cases of acute coronary syndrome data with a cross-validation value of 10 fold.

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Using Random Forest Algorithm for Breast Cancer Diagnosis

Cited by 75 publications

References 1 publication

Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

Extensions and adaptations of existing medical information system in order to reduce social contacts during COVID-19 pandemic

Random Forest Algorithm to Investigate the Case of Acute Coronary Syndrome

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