Biomarkers for ovarian cancer for the accurate detection and classification using computational algorithms and interpreting using explainers

Ovarian cancer remains a significant challenge in oncology due to its high mortality rates, often attributed to late-stage diagnosis and lack of effective screening methods. Biomarkers are essential for the precise identification and categorization of ovarian cancer, providing the opportunity for early diagnosis and individualized treatment plans. In this work, we examine how computational methods can be used to better diagnose and classify ovarian cancer by utilizing biomarker data. Through a rigorous assessment of the literature and bioinformatics analysis, a panel of biomarkers with promising uses in diagnosis and prognosis is found. Next, we employ numerous machine learning techniques, including as random forests and support vector machines, and deep learning architectures, to develop prediction models that have been trained using proteomic and genomic data. We use explainable AI techniques, such LIME and SHAP, to improve the interpretability of these models by offering clear insights into the decision-making process. The computational algorithms are effective in precisely identifying and categorizing subtypes of ovarian cancer according to biomarker profiles. Additionally, by establishing a correlation between computational predictions and treatment outcomes and patient outcomes, we clarify the clinical significance of these findings. This work highlights the potential of computational approaches to improve ovarian cancer diagnosis, and highlights the significance of interpretable models in enabling clinical translation and decision-making.