MedTrace Logo

Assessment of Ovarian Cysts Using Machine Learning

NCT05342298 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 1000

Last updated 2022-08-23

No results posted yet for this study

Summary

The study aims at creating a prediction model using machine learning algorithms that is capable of predicting malignant potential of ovarian cysts/masses based on patient characteristics, sonographic findings, and biochemical markers

Conditions

  • Ovarian Cyst

Interventions

OTHER

prediction model

Data will be pre-processed prior to final analysis, including data cleaning, imputation of missing values, dimensionality reduction, and removal of outliers. Data will be utilized as Xi and Yi where Xi presents input (features) and Yi presents dependent variables (outcomes). Different classification algorithms will be tested for accuracy to build the final model including logistic regression, SVM, XGboost and random forest algorithms. Data will be split at 0.8:0.2 for model training and testing, respectively.

Sponsors & Collaborators

  • Assiut University

    lead OTHER

Principal Investigators

  • Sherif Shazly, MSc · Assiut University

Eligibility

Min Age
15 Years
Max Age
80 Years
Sex
FEMALE
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2022-10-01
Primary Completion
2023-06-01
Completion
2023-09-01

Countries

  • Egypt

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05342298 on ClinicalTrials.gov