An Enhanced Ensemble Diagnosis of Cervical Cancer: A Pursuit of Machine Intelligence Towards Sustainable Health

Cervical cancer is a potentially life-threatening disease marked by health practitioners. The late diagnosis and treatment, being quite challenging, stake the precious lives of patients. In both developed undeveloped states, formal screening for identification suffers due to its medical cost, unavailable facilities, society norms, appearance symptoms. Machine intelligence cost-effective, computationally inexpensive, early several types diseases, including cervical cancer. patients are not required pass through contemporary tedious procedures, handy with machine-intelligent solutions. problem current machine classification methods reliance on single classifier’s prediction accuracy. adoption doesn’t ensure optimum bias, over-fitting, mishandling noisy data, outliers. This research study proposes an Ensemble method based majority voting accurate addressing patient’s conditions or experiments wide range available classifiers, namely Decision Tree (DT), Support Vector (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Naive Bayes (NB), Multiple Perceptron (MP), J48 Trees, Logistic Regression (LR) classifiers. records significant enhancement in accuracy 94% that outperforms accuracies tested same benchmarked datasets. Thus, proposed model bestows second opinion practitioners timely treatment.