Early Detection Of Kidney Cancer Using Machine Learning-based Predictive Modeling

Abstract

Kidney Cancer Remains A Serious Health Threat Worldwide, With Many Cases Undetected Until Reaching Advanced Stages, Leading To Reduced Survival Rates. Factors Like Limited Awareness And Insufficient Diagnostic Resources Contribute To High Mortality, Especially In Regions With Restricted Healthcare Access. This Project Presents A Predictive System Designed To Support Early Detection Of Kidney Cancer By Analyzing Clinical And Demographic Data Through Machine Learning. Models Such As Logistic Regression And Neural Networks Were Utilized To Estimate Cancer Risk, Yielding High Accuracy In Detection. By Leveraging Data From Diverse Patient Groups, The System Aims To Provide Healthcare Professionals With A Reliable Tool For Identifying Kidney Cancer At Earlier Stages, Facilitating Timely Intervention And Improving Patient Outcomes. Evaluation Of The Models Was Based On Standard Accuracy Metrics, Ensuring Robustness And Broad Applicability. Future Work Will Focus On Enhancing Model Stability And Integrating It Into Clinical Settings, Positioning This System As A Vital Resource In Early Kidney Cancer Diagnosis And Management.

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