A Healthcare System for Internet of Things (IoT) Application: Machine Learning Based Approach

Abstract

Internet of things (IoT) has become an interesting topic in the field of technological research. It is basically interconnecting of devices with each other over the internet. Beside its general use in terms of autonomous cars and smart homes, but some of the best applications of IoT technology in fields of health care monitoring is worth mentioning. The main purpose of this research work is to provide comport services for patients. It can be used to promote basic nursing care by improving the quality of care and patient safety from patient home environment. Rural area of a country lacks behind the proper patient monitoring system. So, remote monitoring and prescribing by sharing medical information in an authenticated manner is very effective for betterment of medical facilities in rural area. We have proposed a healthcare system which can analyze ECG report using supervise machine learning techniques. Analyzing report can be stored in cloud platform which can be further used to prescribe by the experienced medical practitioner. For performance evaluation, ECG data is analyzed using six supervised machine learning algorithms. Data sets are divided into two groups: 75 percent data for training the model and rest 25 percent data for testing. To avoid any kind of anomalies or repetitions, cross validation and random train-test split was used to obtain the result as accurate as possible.

Efficient Early Detection of Patient Diagnosis and Cardiovascular Disease using an IoT System with Machine Learning and Fuzzy Logic

“…The experimental findings demonstrate that the proposed method outperforms the conventional disease prediction techniques. Some studies have also used fuzzy logic [9], [10] and machine learning approaches to diagnose healthcare systems [11], [12], [13]. Rahman et al [9] This study uses ECG data to focus on how intelligent systems and fuzzy logic can detect and classify significant cardiac arrhythmias in individuals, particularly those with COVID-19.…”

“…Application of health diagnosis features and healthcare research in health diagnosis. Abdullah et al [12] provided A medical facility that employs supervised learning algorithms to evaluate Electrocardiogram data; development of a safe IoT-based medicare system for monitoring that predicts cardiac ailments using Machine learning prediction algorithms. Godi et al [13] describe an E-Health care monitoring system (EHMS) that uses the IoT wearable technology and algorithms for learning for online patient health tracking and check-ups.…”

Energy Efficient Clustering and Routing Using Hybrid Fuzzy with Modified Rider Optimization Algorithm in IoT - Enabled Wireless Body Area Network

“…The [12] presented a comprehensive overview of these machine learning algorithms, which can be used to improve applications' intelligence and functionality. As a result, the primary contribution of this study is to explain the principles of various machine learning techniques and their applicability to a variety of practical application domains such as cyber security systems, smart cities, healthcare, e-commerce, and agriculture.…”

“…Instead of minimizing each fitness function individually, Equation shows how to reduce a combination of the above functions (19). The fitness functions described above are highly consistent with one another.The following fitness function is used: Fitness function=a*f1 +b*f2 +c*f3 +(1−a+b+c)*f4 (12) where a, b, and c are constants and a + b + c = 1. The flowchart of the proposed algorithm is given as follows:…”

Detection of Middlebox-Based Attacks in Healthcare Internet of Things Using Multiple Machine Learning Models

“…To solve this problem, an intrusion detection and prevention system must be widely deployed across the network. There has been some development in the design of IDSs, but despite this, intrusion detection remains a difficult challenge [ 6 , 18 ] [ 19 ] due to the vast volume of network traffic, the variety of network features available, and the plethora of attacking patterns. It is obvious that false-negative mistakes could happen when using network-based detection algorithms.…”