A Novel Machine Learning Technique for Diabetic Prediction in IoT-based Healthcare Monitoring System

S. A. V. Jesuraj, R. Ganesh, Vinda Manjramkar, M. Sridharan, V. Dubey, M. Arun
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Abstract

The successful development of a diagnosis system to identify diabetes in the Internet of Things (IoT) e-healthcare scenario has gained considerable attention to implement accurate diabetes diagnosis. IoT is playing an increasingly important role in healthcare environment by providing a structure for evaluating medical information to detect diseases via data mining techniques. The existing diagnostic methods have some challenges, such as lengthy calculation times and inaccurate predictions. To evade the limitations, this article suggested an IOT-based diagnosis system that uses Machine Learning (ML) techniques. Through the dataset from UCI Repository with medical sensors, a novel systematic technique is utilized to treat diabetic disease, and relevant medical data is produced to precisely predict those who would be seriously impacted by the condition. For predicting the illness and its severity, a brand-new classification technique called Tuna Swarm optimization-Aided Neural Classifier (TSO-NN) is suggested (T. The experimentation is conducted in MATLAB and the performance is evaluated using accuracy, precision, and F1-score. Besides, the efficiency is tested and confirmed by comparing over SOTA methods.
基于物联网的医疗监测系统中糖尿病预测的新型机器学习技术
物联网(IoT)电子医疗场景中糖尿病诊断系统的成功开发已经引起了人们的广泛关注,以实现准确的糖尿病诊断。物联网在医疗环境中发挥着越来越重要的作用,它通过数据挖掘技术提供了评估医疗信息以检测疾病的结构。现有的诊断方法存在计算时间长、预测不准确等问题。为了规避这些限制,本文提出了一种基于物联网的诊断系统,该系统使用机器学习(ML)技术。通过UCI Repository的数据集和医学传感器,利用一种新的系统技术来治疗糖尿病疾病,并产生相关的医学数据来精确预测哪些人会受到糖尿病的严重影响。为了预测疾病及其严重程度,提出了一种全新的分类技术——金枪鱼群优化辅助神经分类器(TSO-NN) (T.)。在MATLAB中进行了实验,并通过准确性、精密度和f1评分对其性能进行了评价。通过与SOTA方法的比较,验证了该方法的有效性。
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