Prediction of Malignant and Benign Breast Cancer: A Data Mining Approach in Healthcare Applications

Kumar, Vivek; Mishra, Brojo Kishore; Mazzara, Manuel; Thanh, Dang N. H.; Verma, Abhishek

doi:10.1007/978-981-15-0978-0_43

Cited by 70 publications

(40 citation statements)

References 11 publications

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“…Recently, a host of cancer studies have been underpinned by bioinformatics analyses which could help to explore the molecular mechanisms of carcinogenesis 8‐11 . The Oncomine database (https://www.oncomine.org) is a publicly accessible online cancer microarray database for the purpose of gene search function in cancers 12 .…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a host of cancer studies have been underpinned by bioinformatics analyses which could help to explore the molecular mechanisms of carcinogenesis. [8][9][10][11] The Oncomine database (https://www.oncomine.org) is a publicly accessible online cancer microarray database for the purpose of gene search function in cancers. 12 Indeed, a great range of lung cancer research was involved in this database, such as the prognostic roles of KIF23 in NSCLC 13 and the clinical significance of HOXA13 in LUAD.…”

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confidence: 99%

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PYCR1 knockdown inhibits the proliferation, migration, and invasion by affecting JAK/STAT signaling pathway in lung adenocarcinoma

Gao

Luo

Xie

et al. 2020

Molecular Carcinogenesis

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Lung adenocarcinoma (LUAD), as a form of non‐small cell lung cancer (NSCLC), is the most frequently diagnosed lung cancer worldwide. To date, a few biomarkers have been reported to provide valuable information in guiding LUAD treatment. The aim of our study was to explore the functional role of pyrroline‐5‐carboxylate reductase 1 (PYCR1) in LUAD. Based on Oncomine database, we found that PYCR1 was highly expressed in LUAD tissues. We also confirmed an abnormal increase of PYCR1 expression in LUAD cell lines and patients' tissues. Through Kaplan‐Meier plotter database, we further studied the prognostic values of PYCR1. The outcomes indicated that overexpressed PYCR1 associated with poor prognosis among LUAD patients. To further study the function of PYCR1 in LUAD, cell counting kit‐8, colony‐forming, scratch wound healing, and Transwell assays were conducted. The results suggested that knockdown of PYCR1 curbed cell proliferation, migration, and invasion in LUAD cell lines. Subsequently, we identified 50 top genes positively and negatively correlated with PYCR1 in LUAD, and conducted biological pathway enrichment analysis of these genes. Among those enriched pathways, we selected JAK/STAT signaling pathway for further analysis. The results of Western blot assays revealed that PYCR1 knockdown significantly increased the expression of Bcl‐2 and c‐Myc, and the phosphorylation level of JAK2 and STAT3. Taken together, this study unearthed that PYCR1 knockdown could inhibit tumor growth and affect the JAK/STAT signaling pathway in LUAD. This study may contribute to a better understanding of PYCR1 in LUAD and provide a potential biomarker for cancer prognosis.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

PYCR1 knockdown inhibits the proliferation, migration, and invasion by affecting JAK/STAT signaling pathway in lung adenocarcinoma

Gao

Luo

Xie

et al. 2020

Molecular Carcinogenesis

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“…In a study [16] had an attempt to diagnose the breast cancer, using twelve data mining algorithms, including J48, Naïve Bayes and Random Forest. The data set used in this study is derived from UCI website, with 699 records and 10 attributes.…”

Section: Review Of Previous Methodsmentioning

confidence: 99%

Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

2019

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Breast cancer is the second leading cancer among women worldwide, the incidence of which has been reported to increases every year due to factors such as inheritance, lifestyle, and dietary habits [1]. Industrialization and urbanization are among the other contributing factors to the increase incidence of breast cancer. On the other hand, new facilities have been used for the early diagnosis of this chronic disease. The prevalence rate of breast cancer has been reported to be higher in high-income countries, while the incidence rate is on the rise in low-income countries as well, such as the regions in Africa, Asia, and Latin America [2]. In India, the mean age range of the high-risk population for breast cancer has been estimated at 43-46 years, and women aged 53-57 years are reported to be more susceptible to breast cancer [3].

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“…The work by [10] used 12 different machine learning techniques for the diagnosis of breast cancer. The techniques that were used are namely; NB, Decision Table, Ada Boost M1, J48, J-Rip, Logistics Regression, Lazy IBK, Lazy K-star, Multiclass Classifier, Multilayer-Perceptron, RF, and RT.…”

Section: Related Workmentioning

confidence: 99%

“…However, not all mentioned previous works that used WEKA tools for data mining, the data mining using WEKA tools, achieved the same task. For example, [10], [13] and [14] used data mining methods to classify cases of breast cancer into malignant and benign. Moreover, the other studies did experiments on a few techniques while this study tested thirteen different algorithms.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Data Mining Techniques on Breast Cancer Diagnosis Data using WEKA Toolbox

Alshammari¹,

Mezher²

2020

IJACSA

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Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to perform a data mining practical experiment using the Waikato Environment for Knowledge Analysis (WEKA) tool. The WEKA Java application represents a rich resource for conducting performance metrics during the execution of experiments. Pre-processing and feature extraction are used to optimize the data. The classification process used in this study was summarized through thirteen experiments. Additionally, 10 experiments using various different classification algorithms were conducted. The introduced algorithms were: Naïve Bayes, Logistic Regression, Lazy IBK (Instance-Bases learning with parameter K), Lazy Kstar, Lazy Locally Weighted Learner, Rules ZeroR, Decision Stump, Decision Trees J48, Random Forest and Random Trees. The process of producing a predictive model was automated with the use of classification accuracy. Further, several experiments on classification of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer, were conducted to compare the success rates of the different methods. Results conclude that Lazy IBK classifier k-NN can achieve 98% accuracy among other classifiers. The main advantages of the study were the compactness of using 13 different data mining models and 10 different performance measurements, and plotting figures of classifications errors.

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Prediction of Malignant and Benign Breast Cancer: A Data Mining Approach in Healthcare Applications

Cited by 70 publications

References 11 publications

PYCR1 knockdown inhibits the proliferation, migration, and invasion by affecting JAK/STAT signaling pathway in lung adenocarcinoma

PYCR1 knockdown inhibits the proliferation, migration, and invasion by affecting JAK/STAT signaling pathway in lung adenocarcinoma

Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

A Comparative Analysis of Data Mining Techniques on Breast Cancer Diagnosis Data using WEKA Toolbox

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