Silent Sentinel: The Unseen Battle of Prostate Cancer Early Diagnosis with Advanced Artificial Neural Network Technology

Abstract

Introduction: The condition of prostate cancer continues to represent a substantial medical issue because it necessitates precise predictive models that support healthcare decisions. A research study investigates how Extreme Gradient Boosting (XGBoost) performs when identifying significant biomarkers to anticipate prostate cancer risks. The research compares XGBoost to Sequential Minimal Optimization (SMO) in Support Vector Machines (SVM) to evaluate its better performance in classification. Prostate-Specific Antigen (PSA) levels together with Gleason scores and molecular markers form the clinical parameters within the dataset. Process data correctly by cleansing it and normalizing it while extracting important features to achieve the optimal model performance.

Objectives: This research investigates how well Extreme Gradient Boosting (XGBoost) predicts prostate cancer through an examination of its function to discover complex non-linear data patterns in clinical datasets. The research compares XGBoost to SMO algorithm of Support Vector Machines through a performance assessment of classification metrics including accuracy, precision, recall and F1-score. The research explores two important biomarkers Prostate-Specific Antigen (PSA) levels along with Gleason scores which affect how prostate cancer is expected to progress. The research applies machine learning advanced methods to develop improved early identification systems that lead to better clinical decisions which subsequently enhance patient health results.

Methods: The research method relies on a structured machine learning process where multiple steps start with detailed data preprocessing for inconsistent data elimination and variable normalization. Feature extraction technologies increase model interpretability by being applied to the analysis. The available dataset contains two well-separated sections for training and testing purposes which aims to establish reliable validation procedures. Both XGBoost and SVM with SMO operate for classification tasks while their achievement levels are assessed through standard measurement criteria which include accuracy, precision, recall and F1-score. A logistic regression analysis validates whether PSA levels and Gleason scores are significant factors in predictive modeling.

Results: XGBoost achieves superior results over SVM with SMO according to every mitigation evaluation indicator. The classification metric for XGBoost displays 0.95 accuracy whereas SVM with SMO achieves 0.87 accuracy. The evaluation of XGBoost produced precision measurements of 0.92 while recall reached 0.93 and F1-score achieved 0.92. These evaluation scores surpassed SVM with SMO which generated precision of 0.85, recall of 0.80 and F1-score of 0.82. XGBoost manages to lower the chances of overfitting while improving biomarker interpretation so it shows clear potential for clinical prostate cancer prediction applications.

Conclusions: The conducted research demonstrates XGBoost's effectiveness for prostate cancer classification through better prediction accuracy along with stronger resistance than SVM with SMO. The research demonstrates AI-powered models should become an integral part of clinical practice to improve the detection and treatment preparation for patients.