Recent Papers

Among the various types of diseases, cancer is considered as one of the deadly diseases in the world. lung, prostate, and breast cancer are some of the cancer types that are contributing most to the mortality rate. In order to overcome this, our proposed research work aims at analyzing the performance of various machine learning algorithms in the early detection of three types of cancers namely breast cancer, lung cancer and prostrate cancer. The machine learning models were evaluated as well as compared based on Performance Metrics parameters like Accuracy, Precision, Recall, F1Score. The experimental results suggest that the Logistic Regression offers the highest Accuracy for the prediction of Lung Cancer and the Decision Tree for the prediction of Prostate Cancer and Breast Cancer. The proposed framework has been integrated with python script through the Python Flask Framework. This allows the framework to fetch the user inputsresponses with the help of forms and according to the user inputs the framework will select appropriate classifier and will give the result as benign or malignant. Hence, this framework would assist the physicians as well as the users in the early detection of cancer which serve as a warning signal for further investigation, treatment and mitigation of cancer at early stages. This will in turn help in reducing the mortality rate of cancer patients.

Copyright © 2023 P. D. Sheba Kezia Malarchelvi. This is an open access article distributed under the Creative Commons Attribution License.