Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Correction: Javaid et al. WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases. <i>Int. J. Environ. Res. Public Health</i> 2023, <i>20</i>, 3740.

International journal of environmental research and public health·2025
Same author

Correction: Rashid et al. A Minority Class Balanced Approach Using the DCNN-LSTM Method to Detect Human Wrist Fracture. <i>Life</i> 2023, <i>13</i>, 133.

Life (Basel, Switzerland)·2025
Same author

Correction: Zafar et al. Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey. <i>Life</i> 2023, <i>13</i>, 146.

Life (Basel, Switzerland)·2025
Same author

Correction: Almadhor et al. AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery. <i>Sensors</i> 2021, <i>21</i>, 3830.

Sensors (Basel, Switzerland)·2025
Same author

Correction: Oluwasanmi et al. Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction. <i>Sensors</i> 2023, <i>23</i>, 3836.

Sensors (Basel, Switzerland)·2025
Same author

A Dynamic Traffic Light Control Algorithm to Mitigate Traffic Congestion in Metropolitan Areas.

Sensors (Basel, Switzerland)·2024
Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026
Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026
Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026
Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026
Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026
Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 8, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis.

Muniba Saleem1, Waqar Aslam2, Muhammad Ikram Ullah Lali3

  • 1Department of Computer Science & Information Technology, The Government Sadiq College Women University Bahawalpur, Bahawalpur 63100, Pakistan.

Diagnostics (Basel, Switzerland)
|November 24, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning models enhance thalassemia detection. Feature selection and classification techniques, including SMOTE and Gradient Boosting, significantly improve diagnostic accuracy for alpha-thalassemia patients.

Keywords:
classificationfeature selectionfilter-basedthalassemiawrapper and embedded method

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

768

Related Experiment Videos

Last Updated: Jun 8, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

768

Area of Science:

  • Genetics and Hematology
  • Computational Biology and Machine Learning

Background:

  • Thalassemia is a prevalent global genetic disorder impacting hemoglobin synthesis, causing chronic hemolytic anemia and iron overload.
  • Despite challenges, advancements in diagnosis and therapy have improved patient outcomes.
  • Accurate detection is crucial for effective management and treatment strategies.

Purpose of the Study:

  • To evaluate machine learning classification models for thalassemia detection.
  • To identify effective features for improving diagnostic accuracy.
  • To assess the performance of various feature selection and classification techniques.

Main Methods:

  • Employed five feature selection methods: Chi-Square (χ2), Exploratory Factor Score (EFS), Recursive Feature Elimination (RFE), gradient-based RFE, and Linear Regression Coefficient.
  • Utilized nine classifiers: KNN, DT, GBC, LR, AdaBoost, XGB, RF, LGBM, and SVM.
  • Investigated the impact of over-sampling techniques like SMOTE combined with RFE and cross-validation for alpha-thalassemia detection.

Main Results:

  • The Chi-Square (χ2) feature selection method with the Linear Regression (LR) classifier achieved 91.56% precision, 91.04% recall, and 92.65% f-score.
  • Combining SMOTE, RFE, and 10-fold cross-validation significantly boosted detection accuracy for alpha-thalassemia (αT) patients.
  • The Gradient Boosting Classifier (GBC) demonstrated high performance with 93.46% accuracy, 93.89% recall, and 92.72% F1 score.

Conclusions:

  • Machine learning, particularly feature selection and classification algorithms, offers a powerful approach for accurate thalassemia diagnosis.
  • Optimized feature sets and advanced classification models like GBC can significantly enhance early detection and management of thalassemia.
  • The study highlights the potential of integrating techniques like SMOTE and RFE for improving diagnostic precision in genetic blood disorders.