Breast Cancer Detection in the IOT Health Environment Using Modified Recursive Feature Selection

Muhammad Hammad Memon,Wang Zhou,Muhammad Hunain Memon,Amin Ul Haq,Jian Ping Li

doi:10.1155/2019/5176705

Abstract

The accurate and efficient diagnosis of breast cancer is extremely necessary for recovery and treatment in early stages in IoT healthcare environment. Internet of Things has witnessed the transition in life for the last few years which provides a way to analyze both the real-time data and past data by the emerging role of artificial intelligence and data mining techniques. The current state-of-the-art method does not effectively diagnose the breast cancer in the early stages, and most of the ladies suffered from this dangerous disease. Thus, the early detection of breast cancer significantly poses a great challenge for medical experts and researchers. To solve the problem of early-stage detection of breast cancer, we proposed machine learning-based diagnostic system which effectively classifies the malignant and benign people in the environment of IoT. In the development of our proposed system, a machine learning classifier support vector machine is used to classify the malignant and benign people. To improve the classification performance of the classification system, we used a recursive feature selection algorithm to select more suitable features from the breast cancer dataset. The training/testing splits method is applied for training and testing of the classifier for the best predictive model. Additionally, the classifier performance has been checked on by using performance evaluation metrics such as classification, specificity, sensitivity, Matthews’s correlation coefficient, F1-score, and execution time. To test the proposed method, the dataset “Wisconsin Diagnostic Breast Cancer” has been used in this research study. The experimental results demonstrate that the recursive feature selection algorithm selects the best subset of features, and the classifier SVM achieved optimal classification performance on this best subset of features. The SVM kernel linear achieved high classification accuracy (99%), specificity (99%), and sensitivity (98%), and the Matthews’s correlation coefficient is 99%. From these experimental results, we concluded that the proposed system performance is excellent due to the selection of more appropriate features that are selected by the recursive feature selection algorithm. Furthermore, we suggest this proposed system for effective and efficient early stages diagnosis of breast cancer. Thus, through this system, the recovery and treatment will be more effective for breast cancer. Lastly, the implementation of the proposed system is very reliable in all aspects of IoT healthcare for breast cancer.

Highlights

Breast cancer (BC) is the most critical and common disease which greatly a ected ladies in the world according to American Institute for Cancer Research [1], and there were 2 million new cases in 2018
Classification Results of support vector machine (SVM) (Linear). e SVM predictive model performance have been checked for prediction of breast cancer on the fullfeature set and on different selected feature subsets which are produced by recursive feature selection algorithm (REF) feature selection (FS) algorithm and tabulated in Table 3. e SVM parameters C = 1 and c = 0.0001 values are used in all our experiments. e performance evaluation metrics are automatically computed and tabulated into Table 4. e SVM linear predictive model performance on a different combination of feature subset has been reported into Table 4
Classification Results of SVM (RBF). e SVM predictive model performance has been checked for prediction of breast cancer on the full-feature set and on different selected feature subsets which are selected by REF FS algorithm. e SVM parameters C = 1 and c = 0.0001 values are used in all our experiments

Summary

Introduction

To take a biopsy from the breast is painful for the patient Another breast cancer diagnosis technique is mammogram [4] which is used for the diagnosis of breast cancer. In this technique, a 2-dimensional (2D) projection image of the breast is designed. The mammogram technique does not perform the diagnosis of benign cancer effectively. Another invoice-based technique for the diagnosis of the breast is magnetic reasoning imaging (MRI) [5], which is a very complex test and provides excellent results for 3-dimensional (3D) images and displays the dynamic functionality

Methods

Results

Conclusion