Role of Biochemical Markers in Early Detection and Prognostication of Cancer Pathology

Abstract

Biochemical markers play a crucial role in cancer early detection and prognosis, and this study delves into the crucial field of cancer pathology. The study employs a multifaceted strategy by integrating a variety of datasets, including clinical, imaging, and transcriptomic data. To unravel the intricate relationships in the datasets, four leading algorithms—Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and Gradient Boosting—are utilized, revealing the distinct advantages of each. The trials, driven by a fastidious preprocessing of information, reveal convincing bits of knowledge. When it comes to deciphering the complexities of transcriptomic data, SVM demonstrates exceptional accuracy (89 percent) and precision (91 percent). RF is adaptable, achieving precision of 93% and accuracy of 92% across a variety of data types. CNN, custom-made for picture examination, achieves an exemplary exactness of 91% and accuracy of 92%. The outfit learning approach of Inclination Helping yields vigorous outcomes, accomplishing an exactness of 90% and accuracy of 92%. Accuracy, precision, recall, and AUC-ROC comparisons highlight the nuances of each algorithm's strengths. SVM succeeds in high-layered transcriptomic information, RF features adaptability, CNN succeeds in picture examination, and Slope Supporting displays vigorous execution. This study contributes to the evolving landscape of cancer research by aligning its findings with related work and highlighting the requirement for tailored algorithm selection based on data characteristics.