Early Disease Detection Using Ensemble Method

This paper proposes an ensemble-based machine learning model for early disease detection using AdaBoost integrating Support Vector Machines, Decision Trees, and Random Forest classifiers. To pick the most useful signs within health records, tools like Fisher’s Score, Information Gain, plus Genetic Algorithms search help trim down inputs. One tricky part – uneven groups in data – gets fixed by SMOTE. Testing happens across fifteen rounds of validation using real collections pulled from public sources online. When scores roll in, the ensemble approach beats older standalone models across every mark: how often it is right, how clean its positives are, how much it catches, balance in both, and area under curve too.