Advancements in Machine Learning and Deep Learning for Breast Cancer Detection: A Systematic Review DOI Creative Commons

Zeba Khan,

Madhavidevi Botlagunta,

Gorli L. Aruna Kumari

et al.

Artificial intelligence, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 19, 2024

Breast cancer is a significant transnational health concern, requiring effective timely detection methods to improve patient’s treatment result and reduce mortality rates. While conventional screening like mammography, ultrasound, MRI have proven efficacy, they possess limitations, such as false-positive results discomfort. In recent years, machine learning (ML) deep (DL) techniques demonstrated potential in transforming breast through the analysis of imaging data. This review systematically explores advancements research applications for detecting cancer. Through systematic existing literature, we identify trends, challenges, opportunities development deployment ML DL models diagnosis. We highlight crucial role early enhancing patient outcomes lowering Furthermore, impact technologies on clinical procedure, outcomes, healthcare delivery detection. By identifying evaluating studies detection, aim provide valuable insights researchers, clinicians, policymakers, stakeholders interested leveraging advanced computational enhance

Language: Английский

Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification DOI Creative Commons
Yu Zhu, Mingxu Zhang, Qing Huang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 30, 2025

Abstract The classification of chronic diseases has long been a prominent research focus in the field public health, with widespread application machine learning algorithms. Diabetes is one high prevalence worldwide and considered disease its own right. Given nature this condition, numerous researchers are striving to develop robust algorithms for accurate classification. This study introduces revolutionary approach accurately classifying diabetes, aiming provide new methodologies. An improved Secretary Bird Optimization Algorithm (QHSBOA) proposed combination Kernel Extreme Learning Machine (KELM) diabetes prediction model. First, (SBOA) enhanced by integrating particle swarm optimization search mechanism, dynamic boundary adjustments based on optimal individuals, quantum computing-based t-distribution variations. performance QHSBOA validated using CEC2017 benchmark suite. Subsequently, used optimize kernel penalty parameter $$\:C$$ bandwidth $$\:c$$ KELM. Comparative experiments other models conducted datasets. experimental results indicate that QHSBOA-KELM model outperforms comparative four evaluation metrics: accuracy (ACC), Matthews correlation coefficient (MCC), sensitivity, specificity. offers an effective method early diagnosis diabetes.

Language: Английский

Citations

1
Enhanced Leukemia Prediction using Hybrid Ant Colony and Ant Lion Optimization for Gene Selection and Classification DOI Creative Commons

D Santhakumar,

Gnanajeyaraman Rajaram,

R Elankavi

et al.

MethodsX, Journal Year: 2025, Volume and Issue: 14, P. 103239 - 103239

Published: Feb. 20, 2025

Language: Английский

Citations

1
Transforming Cancer Classification: The Role of Advanced Gene Selection DOI Creative Commons
Abrar Yaqoob, Mushtaq Ahmad Mir, G. Venkata Rao

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(23), P. 2632 - 2632

Published: Nov. 22, 2024

Accurate classification in cancer research is vital for devising effective treatment strategies. Precise depends significantly on selecting the most informative genes from high-dimensional datasets, a task made complex by extensive data involved. This study introduces Two-stage MI-PSA Gene Selection algorithm, novel approach designed to enhance accuracy through robust gene selection methods.

Language: Английский

Citations

8
ADVANCED GENETIC ALGORITHM (GA)-INDEPENDENT COMPONENT ANALYSIS (ICA) ENSEMBLE MODEL FOR PREDICTING TRAPPED HUMANS THROUGH HYBRID DIMENSIONALITY REDUCTION DOI Creative Commons

Enoch Adama Jiya,

Ilesanmi B. Oluwafemi

Scientific African, Journal Year: 2025, Volume and Issue: unknown, P. e02564 - e02564

Published: Jan. 1, 2025

Language: Английский

Citations

0
CICADA (UCX): A Novel Approach for Automated Breast Cancer Classification through Aggressiveness Delineation DOI
Davinder Paul Singh, Tathagat Banerjee, Prabhjot Kour

et al.

Computational Biology and Chemistry, Journal Year: 2025, Volume and Issue: 115, P. 108368 - 108368

Published: Feb. 1, 2025

Language: Английский

Citations

0
Hybrid feature selection module for improving performance of software vulnerability severity prediction model on textual dataset DOI
Ruchika Malhotra,

Vidushi

Computing, Journal Year: 2025, Volume and Issue: 107(2)

Published: Feb. 1, 2025

Language: Английский

Citations

0
Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence DOI Creative Commons
Lesia Mochurad, V. I. Babii,

Yuliia Boliubash

et al.

BMC Medical Informatics and Decision Making, Journal Year: 2025, Volume and Issue: 25(1)

Published: Feb. 7, 2025

The relevance of the study is due to growing number diseases cerebrovascular system, in particular stroke, which one leading causes disability and mortality world. To improve stroke risk prediction models terms efficiency interpretability, we propose integrate modern machine learning algorithms data dimensionality reduction methods, XGBoost optimized principal component analysis (PCA), provide structuring increase processing speed, especially for large datasets. For first time, explainable artificial intelligence (XAI) integrated into PCA process, increases transparency interpretation, providing a better understanding factors medical professionals. proposed approach was tested on two datasets, with accuracy 95% 98%. Cross-validation yielded an average value 0.99, high values Matthew's correlation coefficient (MCC) metrics 0.96 Cohen's Kappa (CK) confirmed generalizability reliability model. speed increased threefold OpenMP parallelization, makes it possible apply practice. Thus, method innovative can potentially forecasting systems healthcare industry.

Language: Английский

Citations

0
Prior knowledge evaluation and emphasis sampling-based evolutionary algorithm for high-dimensional medical data feature selection DOI

Zhilin Wang,

Lizhi Shao, Ali Asghar Heidari

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126737 - 126737

Published: Feb. 1, 2025

Language: Английский

Citations

0
Vine Bayes classifier based on truncated copula with application to gene expression data DOI

Tolga Yamut,

Burcu Hüdaverdi

Communications in Statistics - Simulation and Computation, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: Feb. 11, 2025

Language: Английский

Citations

0
Machine Learning in Predicting the Cognitive Improvement of Ventriculoperitoneal Shunt for Chronic Normal Pressure Hydrocephalus After Aneurysmal Subarachnoid Hemorrhage DOI Creative Commons

Youjia Zhu,

Xue Lin, Feng Zhao

et al.

World Neurosurgery, Journal Year: 2025, Volume and Issue: 196, P. 123771 - 123771

Published: March 11, 2025

Chronic normal pressure hydrocephalus (CNPH) is a recognized sequela of aneurysmal subarachnoid haemorrhage (ASAH). Ventriculoperitoneal shunt (VPS) conventional treatment for hydrocephalus, though its effectiveness CNPH post-ASAH remains unclear. We included ASAH patients with who underwent VPS surgery. Changes in the modified Rankin Scale (mRS) before and after surgery were analysed to evaluate benefits. The least absolute shrinkage selection operator (LASSO) identified relevant variables predictive models constructed using eight supervised machine learning algorithms assess benefit. Among 75 (39 males 36 females), 48 (64%) benefited from VPS, while 27 (36%) did not. beneficial group showed longer disease course, higher cerebrospinal fluid (CSF) pressure, lower red white blood cell counts CSF Fisher (MF) Hunt-Hess (HH) grades compared non-beneficial group. Univariate logistic regression analysis indicated that RBC/WBC CSF, WBC count blood, MF grade, HH grade preoperative mRS associated favourable outcomes. Xtreme Gradient Boosting (XGB) model demonstrated highest area under curve (AUC) 0.946 lowest residual error. A nomogram was subsequently developed satisfactory performance. benefits mRS. XGB optimal performance, an AUC 0.946.

Language: Английский

Citations

0