A New Intelligent Approach for Effective Recognition of Diabetes in the IoT E-HealthCare Environment

Abstract

A significant attention has been made to the accurate detection of diabetes which is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the IoT e-healthcare environment. Internet of Things (IOT) has emerging role in healthcare services which delivers a system to analyze the medical data for diagnosis of diseases applied data mining methods. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a IOT based diagnosis system using machine learning methods, such as preprocessing of data, feature selection, and classification for the detection of diabetes disease in e- healthcare environment. Model validation and performance evaluation metrics have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning Decision Tree algorithms, such as Ada Boost and Random Forest are also used for feature selection and compared the classifier performance with wrapper based feature selection algorithms also. Machine learning classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the Decision Tree algorithm based on selected features improves the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high as compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and GL, DPF, and BMI are more significantly important features in the dataset for prediction of diabetes disease. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would be effectively detected diabetes disease and can easily be deployed in IOT wireless sensor technologies based e-healthcare environment.

Analyzing the Digital Pathology Images Using Optimized Generative Adversarial Deep Learning Networks

“…This section describes the performance analysis of PK-BGAN classifier in tumor prediction, the excellence of the tumor identification is determined with various evaluation metrics [34,35] such as accuracy, sensitivity, and f-score. The PK-BGAN model has been implemented in python's keras API.…”

SACDNet: Towards Early Type 2 Diabetes Prediction with Uncertainty for Electronic Health Records

“…Previous studies show higher comorbidity of T2DM with various diseases including cardiovascular disease, hypertension, angina, gastric ulcer, and hypothyroidism [8,9,10,11,12]. It can cause serious long-term complications like affecting the kidneys, eyes, and nerves [13,14]. Also, it increases the risk of heart disease and stroke [15].…”

“…Previous studies have also applied machine and deep learning techniques to datasets other than PIMA [51,13,16,8,42,5,52,12]. For example, NHANES dataset [53] was used to train an SVM-based model for predicting diabetes [54].…”

“…Similarly, another study combined NHANES dataset [53] with lab results of different patients for training Logistic Regression, SVM, Random Forest, and Gradient Boosted Trees for predicting T2DM [50]. Many studies have also collected data consisting of the same features as the PIMA dataset from other sources to build machine learning-based T2DM predictive models [13,37].…”

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