Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

You might also read

Related Articles

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

Sort by

Same author

Effect of Fourth-Line Antihypertensive Therapy on Clinic Systolic Blood Pressure in Resistant Hypertension: A Systematic Review and Meta-Analysis.

Blood pressure·2026

Same author

Clinical Outcomes of GLP-1 Receptor Agonist and SGLT2 Inhibitor Combination Therapy in Heart Failure: A Real-World Propensity-Matched TriNetX Analysis.

Biomedicines·2026

Same author

Cardiovascular Outcomes with Colchicine in Coronary Artery Disease and HFpEF: A Propensity-Matched TriNetX Analysis.

Journal of cardiovascular development and disease·2026

Same author

Temporal trends and forecasted mortality involving lung cancer with co-listed hypertension in U.S. adults, 2000-2035.

Cardio-oncology (London, England)·2026

Same author

Analysis of 173,303 exomes and genomes in the Pakistan Genome Resource.

Nature·2026

Same author

Spectrum of Primary Immunodeficiencies and their Management: Barriers in a Resource-Limited Setting.

Journal of the College of Physicians and Surgeons--Pakistan : JCPSP·2026

Same journal

RETRACTION: An IoMT-Based Approach for Real-Time Monitoring Using Wearable Neuro-Sensors.

Journal of healthcare engineering·2026

Same journal

RETRACTION: Learning to Discriminate Adversarial Examples by Sensitivity Inconsistency in IoHT Systems.

Journal of healthcare engineering·2026

Same journal

RETRACTION: Multi-Chaos-Based Lightweight Image Encryption-Compression for Secure Occupancy Monitoring.

Journal of healthcare engineering·2026

Same journal

RETRACTION: Image Risk Assessment of the Thyroid Cancer Model Based on Discriminant Analysis and the Value of TAP and CEA Combined Detection.

Journal of healthcare engineering·2026

Same journal

RETRACTION: Meta-Analysis of the Prognostic Value of Narcotrend Monitoring of Different Depths of Anesthesia and Different Bispectral Index (BIS) Values for Cognitive Dysfunction after Tumor Surgery in Elderly Patients.

Journal of healthcare engineering·2026

Same journal

Correction to "Representation of Differential Learning Method for Mitosis Detection".

Journal of healthcare engineering·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 19, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Cloud-Based Breast Cancer Prediction Empowered with Soft Computing Approaches.

Farrukh Khan^1,2, Muhammad Adnan Khan^1,3, Sagheer Abbas¹

¹Department of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan.

Journal of Healthcare Engineering

|June 9, 2020

Summary

This summary is machine-generated.

A new cloud-based expert system, BCP-T1F-SVM, aids early breast cancer detection. The BCP-SVM model achieved higher accuracy (97.06%) than BCP-T1F (96.56%) in identifying cancer stage and type.

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Related Experiment Videos

Last Updated: Dec 19, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Oncology

Background:

Developing nations face significant challenges in improving healthcare sectors.
Breast cancer is a prevalent disease among women, with early detection crucial for improved outcomes.
Existing diagnostic methods require enhancement for greater accuracy and efficiency.

Purpose of the Study:

To propose a novel cloud-based intelligent system, BCP-T1F-SVM, for early breast cancer detection and classification.
To evaluate two variations of the system, BCP-T1F and BCP-SVM, for their diagnostic performance.
To accurately determine the stage and type of breast cancer using soft computing algorithms.

Main Methods:

Development of a cloud-based expert system integrating two soft computing algorithms: BCP-T1F and BCP-SVM.
Implementation of the BCP-T1F-SVM system for classifying breast cancer stages and types.
Comparative analysis of the accuracy and precision of BCP-T1F and BCP-SVM models.

Main Results:

The BCP-T1F-SVM system demonstrated effectiveness in diagnosing breast cancer at initial stages.
The BCP-SVM model achieved a higher precision rate compared to the BCP-T1F model.
BCP-T1F achieved 96.56% accuracy, while BCP-SVM achieved 97.06% accuracy.

Conclusions:

The BCP-SVM model is superior to the BCP-T1F model in terms of accuracy for breast cancer detection.
The proposed BCP-T1F-SVM expert system offers a promising approach for early and accurate breast cancer diagnosis.
Findings were supported by medical expertise from Sheikh Zayed Hospital Lahore and Cavan General Hospital.