Published Paper Details:

Health professionals identify cervical cancer as a potentially fatal condition. Patients' valuable lives are at risk due to the difficult late diagnosis and treatment. The formal screening for disease identification suffers in both industrialized and developing countries because of high medical costs, a lack of healthcare facilities, social norms, and the late onset of symptoms. Early diagnosis of various different diseases, including cervical cancer, is possible because to machine intelligence. It is also cost-effective and computationally cheap. Modern, time-consuming medical treatments are not necessary for the patients, and machine intelligence can help with early cervical cancer diagnosis. The reliance on a single classifier's prediction accuracy is the issue with the present machine classification approaches for illness identification. Due to bias, over-fitting, improper treatment of noisy data, and outliers, the use of a single classification method does not guarantee the best prediction. In order to provide an appropriate diagnosis that addresses the patient's symptoms or problems, this research study presents an ensemble classification method based on majority voting. In the study, a variety of classifiers, including Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) classifiers, are put to the test. The study shows an increase in prediction accuracy of 94%, which is significantly higher than the prediction accuracies of individual classification methods tested on the same benchmarked datasets. As a result, the suggested paradigm grants health professionals a second opinion to aid in the early diagnosis and prompt treatment of diseases.