IoT Based Clinical Decision Support System Using Classification Technique

Abstract

In healthcare, different data mining methods are used to mine dataset and then predict diseases using medical data with the help of many machine learning methods. Diabetic disease is spread out in the whole world comprehensively. A prosperous/advanced and skillful method is presented in this research including IoT to gain a better result from the diabetic dataset. The proposed system transmits diabetic data to the database through the cloud system using mobile or smart device or hospital management. If in the dataset has any missing value or abnormal value than the proposed intelligent system will handle it and will predict disease properly. The predicted data are also stored in the database, when users or medical management send a request by legal authentication it will give the predicted results from its system. The proposed system is evaluated using “Pima Indians Diabetes” data set. We use two words, one is RAW data set and another one is NEW data set. Raw data set refers to the “Pima Indians Diabetes Data Set” as it is and the New dataset is the manipulated dataset of the raw dataset. In the new dataset we manipulate some missing and some abnormal value using our technique. In this research, we have improved the accuracy using our technique and we have used several test environments over the research.