Random forest for breast cancer prediction

Octaviani, Theresia Lidya; Rustam, Zuherman

doi:10.1063/1.5132477

Cited by 34 publications

(19 citation statements)

References 8 publications

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“…In earlier studies, the classification of breast cancer images centralized on traditional machine learning methods such as Support Vector Machine (SVM) [ 41 , 42 , 43 ], Naïve Bayes [ 44 , 45 , 46 ], and Random Forest [ 47 , 48 ]. Machine learning involves the algorithms design and deployment to assess data and corresponding attributes without any prior task based on predetermined inputs from the environment [ 49 ].…”

Section: Breast Cancer Image Classificationmentioning

confidence: 99%

Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis

Khairi

Bakar

et al. 2021

Healthcare

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Medical imaging is gaining significant attention in healthcare, including breast cancer. Breast cancer is the most common cancer-related death among women worldwide. Currently, histopathology image analysis is the clinical gold standard in cancer diagnosis. However, the manual process of microscopic examination involves laborious work and can be misleading due to human error. Therefore, this study explored the research status and development trends of deep learning on breast cancer image classification using bibliometric analysis. Relevant works of literature were obtained from the Scopus database between 2014 and 2021. The VOSviewer and Bibliometrix tools were used for analysis through various visualization forms. This study is concerned with the annual publication trends, co-authorship networks among countries, authors, and scientific journals. The co-occurrence network of the authors’ keywords was analyzed for potential future directions of the field. Authors started to contribute to publications in 2016, and the research domain has maintained its growth rate since. The United States and China have strong research collaboration strengths. Only a few studies use bibliometric analysis in this research area. This study provides a recent review on this fast-growing field to highlight status and trends using scientific visualization. It is hoped that the findings will assist researchers in identifying and exploring the potential emerging areas in the related field.

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Section: Breast Cancer Image Classificationmentioning

confidence: 99%

Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis

Khairi

Bakar

et al. 2021

Healthcare

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“…Octaviani [44] proposed a Random Forest classification for predicting breast cancer data. The proposed method was applied to achieve more accurate and reliable classification performance on cancer microarray data.…”

Section: Cancer Classification With Machine Learning Methodsmentioning

confidence: 99%

A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data

Mazlan

Sahabudin²,

Remli³

et al. 2021

Processes

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Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications.

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“…Random forest consists of a single decision tree, which works as an ensemble [48]. This algorithm is extra correct than other classifiers and works expertly on massive datasets.…”

Section: 3random Forestmentioning

confidence: 99%

Machine learning techniques with ANOVA for the prediction of breast cancer

2022

IJATEE

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Breast cancer is one of the most common cancer among females. In this paper, machine learning techniques are applied to molecular taxonomy of breast cancer international consortium (METABRIC) dataset to extract prime clinical attributes to get high accuracy. Analysis of variance (ANOVA), the statistical method, is used for clinical feature selection. Five different machine learning algorithms are implemented, which are support vector machine (SVM), decision tree, random forest, AdaBoost and artificial neural network (ANN). Among all the machine learning classifiers, ANN gives the highest accuracy of 87.43%. This statistical technique is helpful for the detection of breast cancer, and it will increase the survival rate of females.

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Random forest for breast cancer prediction

Cited by 34 publications

References 8 publications

Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis

Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis

A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data

Machine learning techniques with ANOVA for the prediction of breast cancer

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