Comparing Data Mining Algorithms for Breast Cancer Diagnosis

Shanbehzadeh, Mostafa; Nopour, Raoof; Erfannia, Leila; Amraei, Morteza; Mehrabi, Nahid; Mashoufi, Mehrnaz

doi:10.5812/semj-120140

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…Afrash et al used Minimum Redundancy Maximum Relevance (mRMR) feature selection with the ensemble and non-ensemble ML algorithms to diagnose COVID-19 based on clinical data [ 36 ]. Shanbehzadeh et al leveraged ML algorithms and preprocessing steps for breast cancer as a single-centered study approach [ 37 ]. They concluded that using the ML techniques plays a significant role in prediction strategy.…”

Section: Introductionmentioning

confidence: 99%

Screening ovarian cancer by using risk factors: machine learning assists

Nopour

2024

BioMed Eng OnLine

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Background and aim Ovarian cancer (OC) is a prevalent and aggressive malignancy that poses a significant public health challenge. The lack of preventive strategies for OC increases morbidity, mortality, and other negative consequences. Screening OC through risk prediction could be leveraged as a powerful strategy for preventive purposes that have not received much attention. So, this study aimed to leverage machine learning approaches as predictive assistance solutions to screen high-risk groups of OC and achieve practical preventive purposes. Materials and methods As this study is data-driven and retrospective in nature, we leveraged 1516 suspicious OC women data from one concentrated database belonging to six clinical settings in Sari City from 2015 to 2019. Six machine learning (ML) algorithms, including XG-Boost, Random Forest (RF), J-48, support vector machine (SVM), K-nearest neighbor (KNN), and artificial neural network (ANN) were leveraged to construct prediction models for OC. To choose the best model for predicting OC, we compared various prediction models built using the area under the receiver characteristic operator curve (AU-ROC). Results Current experimental results revealed that the XG-Boost with AU-ROC = 0.93 (0.95 CI = [0.91–0.95]) was recognized as the best-performing model for predicting OC. Conclusions ML approaches possess significant predictive efficiency and interoperability to achieve powerful preventive strategies leveraging OC screening high-risk groups.

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Section: Introductionmentioning

confidence: 99%

Screening ovarian cancer by using risk factors: machine learning assists

Nopour

2024

BioMed Eng OnLine

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“…Based on a thermal analysis finite element model of the thermal forming process of ship outer plates, Zhang 15 proposed applying a support vector machine (SVM) to predict local deformation under line heating. Shanbehzadeh 16 conducted a study on early BC prevention based on an ML prediction system and the results showed that machine learning has good predictive power. Nopour 17 chose machine learning techniques to conduct research into the prediction of COVID-19, a model that could help doctors achieve early detection and effective intervention and potentially reduce patient deaths.…”

Section: Introductionmentioning

confidence: 99%

Prediction of line heating deformation on sheet metal based on an ISSA–ELM model

Zhou

et al. 2023

Sci Rep

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A prediction method based on an improved salp swarm algorithm (ISSA) and extreme learning machine (ELM) was proposed to improve line heating and forming. First, a three-dimensional transient numerical simulation of line heating and forming was carried out by applying a finite element simulation, and the influence of machining parameters on deformation was studied. Second, a prediction model for the ELM network was established based on simulation data, and the deformation of hull plate was predicted by the training network. Additionally, swarm intelligence optimization, particle swarm optimization (PSO), the seagull optimization algorithm (SOA), and the salp swarm algorithm (SSA) were studied while considering the shortcomings of the ELM, and the ISSA was proposed. Input weights and hidden layer biases of the ELM model were optimized to increase the stability of prediction results from the PSO, SOA, SSA and ISSA approaches. Finally, it was shown that the prediction effect of the ISSA–ELM model was superior by comparing and analyzing the prediction effect of each prediction model for line heating and forming.

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The Current Progress of Artificial Intelligence in Approach to Thyroid Nodules: A Narrative Review

Yazdanpanahi,

Atighi,

Keshtkar

et al. 2024

Shiraz E-Med J

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Background: Artificial intelligence (AI) can play a significant role in the future of thyroidology. Thyroid nodules are common conditions that may benefit from AI through more accurate and efficient diagnosis, risk stratification, and medical or surgical management. Objective: This paper aims to review the latest developments in AI applications for diagnosing and managing thyroid nodules and cancers. Methods: English full-text articles published in the PubMed and Google Scholar databases from January 2014 to March 2024 were collected and reviewed to provide a comprehensive understanding of the topic. A total of 45 studies were selected based on relevance, robust methodology, statistical significance, and broader topic coverage. Results: Artificial intelligence has emerged as a powerful tool for managing thyroid nodules. First, several studies have demonstrated how AI-powered ultrasound interpretation enhances the diagnosis and classification of nodules while reducing the need for invasive fine-needle aspiration (FNA) biopsies. Second, AI significantly improves the cytopathological differentiation between benign and malignant thyroid nodules by minimizing reliance on pathologists' expertise and implementing standardized diagnostic criteria. When cytopathology is inconclusive, AI also aids in identifying molecular markers from omics data, distinguishing between normal and cancerous cells. Moreover, AI tools have been developed for prognosis assessment, predicting distant metastasis, recurrence, and surveillance by integrating medical imaging features with molecular and clinical factors. Additionally, some AI tools are designed for intraoperative evaluation, improving surgical techniques and reducing complications during thyroidectomy. In non-surgical treatments, several models have been developed to optimize therapeutic doses of radioactive iodine (RAI) and predict the outcomes of new drug formulations. Conclusions: Artificial intelligence has the potential to assist physicians in accurate thyroid nodule diagnosis, classification, decision-making, optimizing treatment strategies, and improving patient outcomes. However, there are still limitations to this technology. Artificial intelligence-driven tools require further advancements before they can be fully integrated into clinical practice and replace specialists.

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Comparing Data Mining Algorithms for Breast Cancer Diagnosis

Cited by 3 publications

References 45 publications

Screening ovarian cancer by using risk factors: machine learning assists

Screening ovarian cancer by using risk factors: machine learning assists

Prediction of line heating deformation on sheet metal based on an ISSA–ELM model

The Current Progress of Artificial Intelligence in Approach to Thyroid Nodules: A Narrative Review

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