Title: Leveraging machine learning with the Internet of things for e-health application

ABSTRACT:
Internet of Things (IoT) empowered numerous applications within the discipline of information technology, intelligent process ability embedded with the networked sensors to improve and analyze the extensive data for diagnosis of diseases. The role of machine learning (ML) is evolving in modern industry 4.0 for predicting uncertainty, establishing inter-correlation and managing comprehensive data. The presented study involves the revolutionary merging of machine learning with IoT for diagnosing and predicting diseases like thoracic cancer, brain tumor, lung cancer, breast cancer, diabetes of levels 1 and 2. The prediction system is assessed (Preprint)
Authors: Gianey Hemant 
Published: May 2023

BACKGROUND

I am Dr. Hemant Kumar Gianey(Associate Professor, VIT University, India) want to submit my research scholar paper. The paper title is " Leveraging machine learning with the Internet of Things for e-health Application." In this paper, we have proposed a novel ML technique on an IoT framework that can be used in the healthcare industry.

OBJECTIVE

The paper title is " Leveraging machine learning with the Internet of Things for e-health Application." This paper proposes a novel ML technique on an IoT framework that can be used in the healthcare industry.

METHODS

Machine Learning
IoT

RESULTS

The proposed and different techniques have been carried out on Intel core i5 processors using python programming. The following subsections depict the experimental results of proposed and various methods like k-nearest neighbors (K-NN), Support vector machine (SVM), decision tree (DT), random forest (RF), and proposed ML models. The comparison among existing and proposed ML procedures is drawn by considering the four notable quality measures: accuracy, f-measure, sensitivity, and specificity. In this study, 10-fold cross-validation is used to beat the under-fitting and over-fitting issues.

CONCLUSIONS

This study propositions a medical care frameworkdependent on a proposed MDE random forest classifier onan IoT framework. The proposed structure will work on the interaction among patients and medical specialists. Exploratory outcomes are led utilizing different datasets identified with various diseases like thoracic cancer, brain tumor, lung cancer, breast cancer, diabetes of level 1 and level 2 to test the proposed model adequacy. The existing examination on e-wellbeing applications has shown that the parameter tuning of current ML techniques is a poorly presented issue. Successful tuning of these parameters can work on the exhibition of existing ML techniques. In this way, to defeat these issues, a novel multi-objective differential evolution based random forest e-health information expectation procedure has been proposed. It has been inferred that the proposed strategy outflanks existing ML models as far as precision, f-measure, affectability and particularity by 1.95%, 1.64%, 1.72% and 1.39%, individually. Subsequently, the proposed strategy is more productive for an ongoing frame e-wellbeing climate. Hence, the revolutionary merging of machine learning with IoT framework may help medical practitioners and patients for better communication and real-time monitoring for diagnosis of diseases and treatment.