Classify Breast Cancer Patients using Hybrid Data-Mining Techniques

Mohammed, Faris E.; Zghal, Nadia Smaoui; Aissa, Dalinda Ben; El-Gayar, Mostafa Mahmoud

doi:10.3844/jcssp.2022.316.321

Cited by 2 publications

(2 citation statements)

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“…Financial institutions increasingly use Machine Learning (ML) techniques (Chen et al, 2021) for stock classification. The advantage of ML and data mining is that it can recognize stock trends from vast volumes of stock price data (Mohammed et al, 2022;Florencio et al, 2019). This study considered technical indicators for stock price classification.…”

Section: Introductionmentioning

confidence: 99%

Stock Price Classification Based on Hybrid Feature Selection Method

Srivinay¹,

Manujakshi²,

Kabadi³

et al. 2023

Journal of Computer Science

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In recent years, investors and traders have used Technical Indicators (TIs) to forecast the stock market. An accurate classification model is required in the stock market to gain more profit. Selecting relevant TIs for the stock market remains a hot research topic. The proposed work aims to identify important technical indicators. Therefore, the proposed work considers a hybrid feature selection method to identify the relevant TIs. The hybrid feature selection combines two individual feature selection methods, such as Boruta and the Random Forest (RF) feature importance method. The work considers 20 TIs. The regression power of TIs is computed using the hybrid feature selection method. Using the hybrid feature selection method, the selected relevant TIs are given as input to the classification model, namely Naive Bayes (NB) and Deep Learning. The classification model aims to classify the stock price as up or down. The Hybrid Feature selection-based Deep Learning H2O model performs better than the hybrid feature selectionbased NB model in the experimental work. The accuracy of the hybrid feature selection of the Deep Learning H2O model is around 86 to 89%. The work considers the National Stock Exchange (NSE) in India for the experimental work.

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Section: Introductionmentioning

confidence: 99%

Stock Price Classification Based on Hybrid Feature Selection Method

Srivinay¹,

Manujakshi²,

Kabadi³

et al. 2023

Journal of Computer Science

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“…The system was tested and evaluated on the Wisconsin BC dataset from the University of Wisconsin Hospitals. Mohammed et al (2022), proposed the enhanced system of a decision support system based on hybrid classification algorithms to classify breast cancer patients accurately and quickly. The main contribution of this article is to develop an algorithm that filters the data and solves the problem of missing data in some records to facilitate the classification of data.…”

Section: Introductionmentioning

confidence: 99%

Radial Basis Function Networks and Contrast-Limited Adaptive Histogram Equalization Filter Based Early-Stage Breast Cancer Detection Techniques

Ponraj¹,

Canessane²

2023

Journal of Computer Science

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Breast cancer is one of the most common types of cancer that kills women. When cells become uncontrollably large, cancer develops. As a result, detecting and classifying breast cancer in its early stages is essential so that patients can take the appropriate precautions. On the other hand, mammography images have relatively low sensitivity and effectiveness in detecting breast cancer. Furthermore, MRI (Magnetic Resonance Imaging) has higher detection sensitivity for breast cancer than mammography. In this research, a novel Radial Basis Function Networks model (RBFN) with a Mayfly Optimization Algorithm (MAO) mechanism has Breast MRI scans aid in the early detection of breast cancer. Following the system's training on Magnetic Resonance Imaging (MRI) breast images, a unique Contrast-Limited Adaptive Histogram Equalization (CLAHE) filter is developed for pre-processing noisy MRI image material. Backgrounds were removed before recovering breast cancer photos with a Contrast Limited Histogram Equalization (CLAHE) filter. Furthermore, the new study effort's performance is compared to earlier studies and this model is simulated using Python. The proposed model, RBFN-MAO, also outperforms previous models in terms of performance and precision with an accuracy of 97.54%. In comparison, it is 85.28, 80.95, 76.94, 85.39 and 90.32% for Convolution Neural Networks You Only Look Once (CNN-YOLO), Residual Networks (ResNet50), Diffusion Convolution Neural Networks (DCNN), Support Vector Machine (SVM) and Convolution Neural Network (CNN) models, respectively.

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Classify Breast Cancer Patients using Hybrid Data-Mining Techniques

Cited by 2 publications

References 0 publications

Stock Price Classification Based on Hybrid Feature Selection Method

Stock Price Classification Based on Hybrid Feature Selection Method

Radial Basis Function Networks and Contrast-Limited Adaptive Histogram Equalization Filter Based Early-Stage Breast Cancer Detection Techniques

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