Breast Cancer Classification: Features Investigation using Machine Learning Approaches

Breast cancer is the second most common cancer after lung cancer and one of the main causes of death worldwide. Women have a higher risk of breast cancer as compared to men. Thus, one of the early diagnosis with an accurate and reliable system is critical in breast cancer treatment. Machine learning techniques are well known and popular among researchers, especially for classification and prediction. An investigation was conducted to evaluate the performance of breast cancer classification for malignant tumors and benign tumors using various machine learning techniques, namely k-Nearest Neighbors (k-NN), Random Forest, and Support Vector Machine (SVM) and ensemble techniques to compute the prediction of the breast cancer survival by implementing 10-fold cross validation. This study used a dataset obtained from Wisconsin Diagnostic Breast Cancer (WDBC) with 23 selected features measured from 569 patients, from which 212 patients have malignant tumors and 357 patients have benign tumors. The analysis was performed to investigate the feature of the tumors based on its mean, standard error, and worst. Each feature has ten properties which are radius, texture, perimeter, area, smoothness, compactness, concavity, concave, symmetry and fractal dimensions. The selection of features was considered a significant influence to the breast cancer. The analysis is compared and evaluated with thirty features to determine the features used for breast cancer classification. The result shown AdaBoost has obtained the highest accuracy for thirty features at 98.95%, ten features of mean at 98.07%, and ten features of worst at 98.77% with a lowest error rate. Additionally, the proposed methods are classified using 2-fold, 3-fold, and 5-fold cross validation to meet the best accuracy rate. Comparison results between all methods show that AdaBoost ensemble methods gave the highest accuracy at 98.77% for 10-fold cross validation, while 2-fold and 3-fold cross validation at 98.41% and 98.24%, respectively. Nevertheless, the result with 5-fold cross validation shows SVM produced the best accuracy rate at 98.60% with the lowest error rate

Human-computer interaction in mobile learning: a review

Mobile learning mainly concerns mobility and high-quality education, regardless of location or time. Humancomputer interaction comprises the concepts and methods in which humans interact with computers, including designing, implementing, and evaluating computer systems that are accessible and provide an intuitive user interface. Some studies showed that mobile learning could help overcome multiple limitations and improve learning in educational systems. The study investigates the HCI design challenges, including the guidelines and methods in mobile HCI for education. An existing mobile learning tool was discussed on the current and future design enhancements of Udemy. Next is the further discussion on future mobile learning to provide the possible improvements for learners based on the challenges of mobile HCI in education

The modelling and simulation of IoT system in healthcare applications

This paper presents a review on modelling and simulation of Internet of things (IoT) in healthcare application. IoT is a large-scale ecosystem of socio-technical application. The emerging technology of IoT has gained enormous attention in a wide range of industries and used in various kind of environments. Healthcare application is one of the vital IoT applications for smart cities. Medical devices and applications of IoT healthcare are emerging rapidly in the global market. Various IoT-based health applications have been proposed to help patients monitor the disease and track the health information without visiting the hospital, clinic, or any medical centre. This paper has briefly described the methods and devices used in IoT-based healthcare application, namely blood pressure monitoring, glucose and cholesterol monitoring, asthma monitoring, and stroke rehabilitation system. The modelling and simulation process of IoT-based healthcare applications are discussed on blood pressure and stroke rehabilitation system only. The development of electrocardiogram (ECG) and Photoplethysmogram (PPG) in blood pressure measurement integration with a smartphone has created simplicity and usability of the device. Nevertheless, further investigation is required to improve the accuracy in collecting the patient’s health information. For hand rehabilitation training purposes, an IoT-enabled stroke rehabilitation device depends on machine learning, smart wearable armband and a 3D printed robot hand were developed to imitate the movements of the patient in a real-time mode. The feature selection used in the development of the device using machine learning has produced a high classification accuracy which helps stroke patients to strengthen the muscles with their motion patterns after stroke