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Detection of Cell-Free DNA in Blood Plasma Samples of Cancer Patients
Published on: September 9, 2020
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Non-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach
Ahmad S Tarawneh1, Ahmad K Al Omari2,3, Enas M Al-Khlifeh4
1Department of Information Technology, Mutah University, Al-Karak, Jordan.
Advances and Applications in Bioinformatics and Chemistry : AABC
|January 16, 2025
Summary
Machine learning models accurately detect various cancers using routine blood test results, including white blood cell counts and platelet counts. This non-invasive approach aids in early cancer screening and diagnosis.
Area of Science:
- Oncology
- Bioinformatics
- Machine Learning
Background:
- Rising cancer incidence presents a significant public health challenge.
- Early and accurate diagnosis is crucial for effective cancer treatment and patient outcomes.
Purpose of the Study:
- To develop a machine learning-driven predictive model for simultaneous diagnosis of multiple cancer types.
- To integrate hematological parameters with machine learning for non-invasive cancer detection.
Main Methods:
- Analysis of a dataset of 19,537 laboratory reports from Jordanian hospitals.
- Data preprocessing including feature standardization and missing value imputation.
- Application of machine learning classifiers such as Random Forest, Linear Discriminant Analysis, Support Vector Machine, and Histogram Gradient Boosting.
Main Results:
- Hematological features like white blood cell count, red blood cell count, and platelet count, along with age and creatinine, were key predictors.
- Random Forest, LDA, and SVM achieved high prediction accuracy (0.69-0.72).
- The Histogram Gradient Boosting model demonstrated improved performance.
Conclusions:
- The integration of hematological indicators and machine learning provides an efficient platform for non-invasive cancer screening.
- Future research exploring deep learning could enhance prediction accuracy by identifying complex patterns.
