Non-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach

Advances and Applications in Bioinformatics and Chemistry, Journal Year: 2025, Volume and Issue: Volume 17, P. 159 - 178

Published: Jan. 1, 2025

Purpose: The incidence of cancer, which is a serious public health concern, increasing. A predictive analysis driven by machine learning was integrated with haematology parameters to create method for the simultaneous diagnosis several malignancies at different stages. Patients and Methods: We analysed newly collected dataset from various hospitals in Jordan comprising 19,537 laboratory reports (6,280 cancer 13,257 noncancer cases). To clean obtain data ready modelling, preprocessing steps such as feature standardization missing value removal were used. Several cutting-edge classifiers employed prediction analysis. In addition, we experimented dataset's values using histogram gradient boosting (HGB) model. Results: ranking demonstrated ability distinguish patients healthy individuals based on hematological features WBCs, red blood cell (RBC) counts, platelet (PLT) addition age creatinine level. random forest (RF) classifier, followed linear discriminant (LDA) support vector (SVM), achieved highest accuracy (ranging 0.69 0.72 depending scenario investigated), reliably distinguishing between malignant benign conditions. HGB model showed improved performance dataset. Conclusion: After investigating number methods, an efficient screening platform non-invasive detection provided integration haematological indicators proper analytical data. Exploring deep methods future work, could provide insights into more complex patterns within dataset, potentially improving robustness predictions. Keywords: learning, complete count, RF model,

Language: Английский

Extended Spectrum beta-Lactamase Bacteria and Multidrug Resistance in Jordan are Predicted Using a New Machine-Learning system

Infection and Drug Resistance, Journal Year: 2024, Volume and Issue: Volume 17, P. 3225 - 3240

Published: July 1, 2024

The incidence of microorganisms with extended-spectrum beta-lactamase (ESBL) is on the rise, posing a significant public health concern. current application machine learning (ML) focuses predicting bacterial resistance to optimize antibiotic therapy. This study employs ML forecast occurrence bacteria that generate ESBL and demonstrate multiple antibiotics (MDR).

Language: Английский