Introduction

The medical services in Bangladesh are shortage nowadays; people are suffering from getting the correct treatment from the hospital. With the low proportion of the doctors and the low per capita salary in Bangladesh, patients need to spend more money to get the appropriate treatments. Therefore, it is necessary to apply modern information technologies by which the scaffold between the patients and specialists can be reduced, and the patients can take proper treatment at a lower cost. Fortunately, we can solve this critical problem by utilizing interaction among electrical devices. With the big data collected from these devices, machine learning is a powerful tool for the data analytics because of its high accuracy, lower computational costs, and lower power consumption. This research is based on a case of study by the incorporation of the database, mobile application, web application and develops a novel platform through which the patients and the doctors can interact. In addition, the platform helps to store the patients’ health data to make the final prediction using machine learning methods to get the proper healthcare treatment with the help of the machines and the doctors. The experiment result shows the high accuracy over 95% of the disease detection using machine learning methods, with the cost 90% lower than the local hospital in Bangladesh, which provides the strong support to implement of our platform in the remote area of the country.

Dataset

We use three popular datasets for breast cancer, heart disease, diabetes, and liver disease detection. Dr. William H. Wolberg created breast cancer data from the University of Wisconsin Hospitals, and the heart data was originated by John Clore from the Center for Clinical and Translational Research, Virginia Commonwealth University. As for liver disease data, Richard S. Forsyth created it from BUPA Medical Research Ltd. In each case, we use 75% of the data for training and the remaining 25% for validation checking.