Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective

Akbulut, Sami; Yağın, Fatma Hilal; Çolak, Cemil

doi:10.4274/imj.galenos.2022.62443

Cited by 4 publications

(4 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It seeks to generate a large number of weak learners in order and incorporate them into a complex model since it is based on the boosting method. Compared to other algorithms, XGBoost has a significant speed and performance advantage [ [36] , [37] , [38] ].…”

Section: Methodsmentioning

confidence: 99%

Explainable artificial intelligence model for identifying COVID-19 gene biomarkers

Yağın

ÇİÇEK

Alkhateeb³

et al. 2023

Computers in Biology and Medicine

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

Explainable artificial intelligence model for identifying COVID-19 gene biomarkers

Yağın

ÇİÇEK

Alkhateeb³

et al. 2023

Computers in Biology and Medicine

View full text Add to dashboard Cite

“…By employing XGB, this study seeks to create a robust and efficient predictive model that can effectively utilize the multitude of hematological parameters to discern patterns and signatures specific to AMI. The strengths of the XGB algorithm lie in its capacity to handle nonlinear relationships, and feature interactions, and its resistance to overfitting, thereby enhancing its suitability for capturing intricate patterns in the data [22,23]. The mathematical equation for the XGB classifier can be represented as follows: Let X be the input feature matrix with 'n' samples and 'm' features: X = [x_1, x_2, ..., x_n] where each x_i is a vector of length 'm'.…”

Section: Classification Methodsmentioning

confidence: 99%

“…By iterative training and reweighting the weak learners, AB creates a robust and accurate ensemble model that effectively captures complex relationships within the hematological parameters and their association with myocardial infectious heart disease. We can represent the AdaBoost algorithm mathematically [22]. Suppose we have a dataset consisting of 'N' samples, where each sample is denoted as xi, and the corresponding target labels as yi, where yi is either 0 (negative class -healthy) or 1 (positive class -AMI).…”

Section: Classification Methodsmentioning

confidence: 99%

Assessment of Hematological Predictors via Explainable Artificial Intelligence in the Prediction of Acute Myocardial Infarction

Yilmaz,

Yagin,

Raza

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

Acute myocardial infarction (AMI) is the main cause of death in developed and developing countries. AMI is a serious medical problem that necessitates hospitalization and sometimes results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Many studies have been conducted on the prognosis of AMI with hemogram parameters. However, no study has investigated potential hemogram parameters for the diagnosis of AMI using an interpretable artificial intelligence-based clinical approach. The purpose of this research is to implement the principles of explainable artificial intelligence (XAI) in the analysis of hematological predictors for AMI. In this retrospective analysis, 477 (48.6%) patients with AMI and 504 (51.4%) healthy individuals were enrolled and assessed in predicting AMI. Of the patients with AMI, 182 (38%) had an ST-segment elevation MI (STEMI), and 295 (62%) had a non-ST-segment elevation MI (NSTEMI). Demographic and hematological information of the patients was analyzed to determine AMI. The XAI approach combined with machine learning approaches (Extreme Gradient Boosting, XGB; Adaptive Boosting, AB; Light Gradient Boosting Machine, LGBM) was applied for the estimation of AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). The SHAP approach was used to explain the predictions intuitively. After selecting the 10 most important hematological parameters for AMI, the LGBM model achieved 83% and 74% accuracy for prediction of AMI, and distinguishing subgroups of AMI (STEMI and NSTEMI), respectively. SHAP results showed that neutrophil (NEU), white blood cell (WBC), platelet width of distribution (PDW), and basophil (BA) were the most important for AMI prediction. Mean corpuscular volume (MCV), BA, monocytes (MO), and lymphocytes (LY) were the most important hematological parameters that distinguish STEMI from NSTEMI. The proposed model serves as a valuable tool for physicians, facilitating the diagnosis, treatment, and follow-up of patients with AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). Analyzing readily accessible hemogram parameters empowers medical professionals to make informed decisions and provide enhanced care to a wide range of individuals.

show abstract

“…MLP is a type of neural network used to support feed forward neural networks. In MLP, the input layer receives the signal to be processed and the output layer does the estimation and classification [20,21].…”

Section: Machine Learning Approachmentioning

confidence: 99%

Genomic Biomarkers of Metastasis in Breast Cancer Patients: A Machine Learning Approach

İNCEOĞLU

Yağın

2022

The Journal of Cognitive Systems

View full text Add to dashboard Cite

One of the cancers with the highest incidence in the world is breast cancer (BC). The aim of this study is to identify candidate biomarker genes to predict the risk of distant metastases in patients with BC and to compare the performance of machine learning (ML) based models. In the study; Genomic dataset containing 24,481 gene expression levels of 97 patients with BC was analyzed. Biomarker candidate genes were determined by ML approaches and models were created with XGBoost, naive bayes (NB) and multilayer perceptron (MLP) algorithms. The accuracy values of XGBoost, NB and MLP algorithms were obtained as 0.990, 0.907 and 0.979, respectively. Our results showed that XGBoost has higher performance. The top five genes associated with BC metastasis were AL080059, Ubiquilin 1, CA9, PEX12, and CCN4. In conclusion, when the ML method and genomic technology are used together, the distant metastasis risk of patients with BC can be successfully predicted. The developed XGBoost model can distinguish patients with distant metastases. Identified biomarker candidate genes may contribute to diagnostic, therapeutic and drug development research in patients with metastases.

show abstract

Prediction of Breast Cancer Distant Metastasis by Artificial Intelligence Methods from an Epidemiological Perspective

Cited by 4 publications

References 39 publications

Explainable artificial intelligence model for identifying COVID-19 gene biomarkers

Explainable artificial intelligence model for identifying COVID-19 gene biomarkers

Assessment of Hematological Predictors via Explainable Artificial Intelligence in the Prediction of Acute Myocardial Infarction

Genomic Biomarkers of Metastasis in Breast Cancer Patients: A Machine Learning Approach

Contact Info

Product

Resources

About