A Classification System for Breast Cancer Prediction using SVOF-KNN method
Ravi Kumar Barwal, Neeraj Raheja · 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · 2022
Cancer plays a vital role in saving the lives of the losses. The research “A Healthcare System based on SVOF-KNN Model for Breast Cancer Classification” aims to classify BC (breast cancer) diagnosis precisely through different Machine Learning steps. It is developed to classify RF's (risk factors) effect in the BC Coimbra dataset connected with a routine BA (blood analysis). The instances and attributes of breast cancer Coimbra dataset are age. BMI, glucosc, Insulin-level in the blood, etc. The Error Rate. RMSE, MAE. SP. and SN are used to calculate the accuracy or prediction of the classifications. The SVOF-KNN evaluates the ER (error rate) values for each feature of the BC Coimbra dataset. It classifies the best attribute as having the minimum ER as the feature for BC diagnosis. SVOF-KNN also organizes the best classification method for BC diagnosis. The results of this proposed model (SVOF-KNN) have improved with 90.3 per cent SP rate and 91.3 per cent SN rate compared with the existing model (KNNBasic).