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

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