E-Healthcare Monitoring System using IoT with Machine Learning Approaches (original) (raw)

Patient health monitoring using IoT with machine learning

2019

Health aspects of human being need to be monitored with utmost care and must be treated with appropriate drugs. Several diseases can be reduced by proactive monitoring of one's health. In the recent decades, technological development is at its peak due to which several wearable devices and health monitoring gadgets are available at the market. Even expert doctors find it challenging to estimate the health issues from the symptoms observed from the diseased. Using modern technological tools such as Internet of Things (IoT), machine learning and Artificial Intelligence along with Big data makes the job of physicians much easier in digging out the root cause of disease and predicting its seriousness using modern algorithms. In this research work, the machine learning algorithms are used to monitor the health conditions of the humans. Initial training and validation of machine learning algorithms are performed using the UCI dataset. Testing phase is carried out by collecting heart rate, blood pressure and temperature of the person using IoT setup. Testing phase estimates the prediction of any abnormalities in the health condition from the sensor data collected through the IoT framework. Statistical analysis is performed from data accumulated into the cloud from IoT device to estimate the accuracy in prediction percentage. Also, from the results obtained from the K-Nearest Neighbor outperforms other conventional classifiers.

Health Care Patient Monitoring using IoT and Machine Learning

IOSR Journal of Engineering (IOSR JEN), 2019

Security and privacy is the most essential thing in Big Data environment, there are many algorithms have been proposed in existing approaches for data privacy as well as security. In many applications like Healthcare are banking applications having available data where third party attacker can easily access the privacy of victims. In Internet of Things (IoT) environment there is the major issue of data security. In this paper we proposed high dimensional Healthcare big data security as well as disease prediction using machine learning approach. Basically the system has categorized into two sections first we implement IoT based environment which generates the data of patient body. This section be used some wearable devices like ECG sensor, BP sensor temperature sensor heart rate sensor etc. Once data has generated from various sensors it will upload on cloud database. In the second phase we monitor the data which is generated by various sensors. Here we have generated Android base graphical user interface with monitors the data 24 by 7. Where machine learning algorithms are has used to predict the disease of patients. The authentication mechanism will achieve role based access control for specific users and proposed machine learning algorithms provides the patient disease probability according to given parameters. The experiment analysis has done based on the partial implementation of system which provide proposed system is more effective than some existing IoT systems..

A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications

IEEE Access, 2021

The Internet of Things (IoT) is playing a vital role in the rapid automation of the healthcare sector. The branch of IoT dedicated towards medical science is at times termed as Healthcare Internet of Things (H-IoT). The key elements of all H-IoT applications are data gathering and processing. Due to the large amount of data involved in healthcare, and the enormous value that accurate predictions hold, the integration of machine learning (ML) algorithms into H-IoT is imperative. This paper aims to serve both as a compilation as well as a review of the various state of the art applications of ML algorithms currently being integrated with H-IoT. Some of the most widely used ML algorithms have been briefly introduced and their use in various H-IoT applications has been analyzed in terms of their advantages, scope, and possible improvements. Applications have been divided into the domains of diagnosis, prognosis and spread control, assistive systems, monitoring, and logistics. In healthcare, practical use of a model requires it to be highly accurate and to have ample measures against security attacks. The applications of ML algorithms in H-IoT discussed in this paper have shown experimental evidence of accuracy and practical usability. The constraints and drawbacks of each of these applications have also been described.

An Architecture of IoT-Aware Healthcare Smart System by Leveraging Machine Learning

The International Arab Journal of Information Technology, 2022

In a healthcare environment, Internet of Things (IoT) sensors’ devices are integrated to help patients and Physicians remotely. Physicians interconnect with their patients to monitor their current health situation. However, a considerable number of real-time patient data produced by IoT devices makes healthcare data intensive. It is challenging to mine valuable features from real-time data traffic for efficient recommendations to patients. Thus, an intelligent healthcare system must analyze the real-time health conditions and predict suitable drugs based on the diseases’ symptoms. In this paper, an IoT architectural model for smart health care is proposed. This model utilizes clustering and Machine Learning (ML) techniques to predict suitable drugs for patients. First, Spark is used to manage the collected data on distributed servers. Second, the K-means clustering algorithm is used for disease-based categorization to make groups of the related features. Third, predictor techniques,...

Analysis of IoT based multi-parameter patient monitoring system using machine learning

THE 2ND UNIVERSITAS LAMPUNG INTERNATIONAL CONFERENCE ON SCIENCE, TECHNOLOGY, AND ENVIRONMENT (ULICoSTE) 2021

One of the applications of machine learning is analysis of a multi-parameter patient monitoring system. A python 3.7 or higher-based system is developed which can be used in paramedicine to execute different ML algorithms with available datasets such as ECG, heartbeat signal, Blood oxygen saturation (SPO2), temperature, and generates different signals from the data set. This Machine learning-based Multi-Parameter Monitoring (MPM) system is designed in which parameters are monitored by utilizing corresponding sensors and analyzing the parameters using different ML algorithms. In the proposed system sensors and hardware parts are omitted and outputs of sensors are directly taken from the available sources for implementation. The project focuses on improving the performance of a multi-parameter patient monitoring system using machine learning classifier algorithms such as Support Vector Machine (SVM), Random Forest, and Naive Bayes classifiers. Although, other ML classifier algorithm are also available, but from practical implementation point of view these three algorithms provide good results. For data analysis, these three-machine learning-based classifier algorithms are used, and datasets are collected online and arranged in a particular sequence. These datasets are trained, tested, and analyzed using machine learning. At last, the results of all classifiers are compared with each other. Based on this comparison, the one with good result is used to decide patient's health condition.

REAL TIME HEALTH MONITORING USING IOT WITH INTEGRATION OF MACHINE LEARNING APPROACH

IAEME PUBLICATION, 2020

Healthiness is the base for every human being. It is directly or indirectly influencing the mental ability of the person. It gives them the confidence to each action of the human. Sound health is necessary to do all our day to day works with the fullest hope. Nowadays all people are having more health- conscious than in the past years. Because of these reasons, there are different types of health check- ups, monitoring clinics are evolved, and they do a lot of monitoring processes like daily, monthly, and master check-ups. To provide multiple services, options, and facilities to their clients the technologies play a vital role in the current era. The rapid development of information technology influences every person's life and health consciousness. These technologies are helping to monitor the status of a person and provide necessary tips then and there. Different methods of check-ups and monitoring process are available to get the information about a person. There are several IoT enabled sensors available to sense the patient complete details about a particular person's behavior, human anatomy, and physiology. This will lead the Big data. The Data gained over the sensors are uploaded to the internet, and connected to the cloud server. The affected person records could be saved in the web server and physicians can get right of entry to the data anywhere in the world. Anyun expected variation in the data of the patient who is using the healthcare system, inevitably the data of the patient will be uploaded to the concerned doctor with immediate notification. This type of health care system will be most useful in rural and remote areas. In this chapter, discussthe Machine learning techniqueswhich are important to the build analysis models. Then howthis model isintegrated with IoT Technology and provide accurate data of individual person and also discuss the Cardiovascular problems based on real-time input data

IOT based Patient Health Monitoring System using ML

International Journal of Engineering and Advanced Technology, 2019

The project focuses on the usage of sensing and analysis with the help of relevant sensor technologies in order to record the health conditions of people. The best way to understand this is with an example. A practising doctor who is not equipped with such technology can check the patients’ health only when the patient pays a visit to the clinic. Now, with the application of the proposed technological measures, the doctor would have a complete record of the patient whether at home, office or on the road, and this would enable him to prescribe medication in a much more efficient and effective manner. Also, it is important to appreciate that on the basis of patient data recorded in the past, a prediction model could help the doctor see irregularities and predict if a patient suffers from commonly occurring ailments hence saving time in an initial diagnosis. This method for Healthcare Data Analytics using Support Vector Machine (SVM) Algorithm helps improve accuracy when it comes to ch...

A Novel Smart Healthcare Monitoring System Using Machine Learning and the Internet of Things

Wireless Communications and Mobile Computing, 2021

The Internet of Things (IoT) has enabled the invention of smart health monitoring systems. These health monitoring systems can track a person’s mental and physical wellness. Stress, anxiety, and hypertension are key causes of many physical and mental disorders. Age-related problems such as stress, anxiety, and hypertension necessitate specific attention in this setting. Stress, anxiety, and blood pressure monitoring can prevent long-term damage by detecting problems early. This will increase the quality of life and reduce caregiver stress and healthcare costs. Determine fresh technology solutions for real-time stress, anxiety, and blood pressure monitoring using discreet wearable sensors and machine learning approaches. This study created an automated artefact detection method for BP and PPG signals. It was proposed to automatically remove outlier points generated by movement artefacts from the blood pressure signal. Next, eleven features taken from the oscillometric waveform envelo...

Machine Learning for IoT HealthCare Applications: A Review

2021

Internet of Things and Machine Learning (ML) have wide applicability in many aspects of life, health care is one of them. With the rapid development and improvement of the internet, the conventional strategies for patient services diminished and supplanted with electronic healthcare systems. The use of IoT technology offers medical professionals and patients the most modern medical device environment. IoT things and Machine-Learning are valuable in various classifications from far off observing of the modern climate to mechanical mechanization. Moreover, medical care applications are principally indicating interest in IoT things in view of cost decrease, easy to understand and improve the personal satisfaction of patients. The latest applications for IoT medical treatment, investigated and still facing problems in the clinical environment, are needed for intellectual, creativity-based answers. In specific, portable, and implantable IoT model devices, investigated for calculating the...

Machine Learning Algorithms for Disease Prediction Using IoT Environment

International Journal of Engineering and Advanced Technology, 2019

In the most advanced healthcare application environment, the use of IoT technologies brings convenience to medical professionals and patients, since they have applied to health areas. In IoT, Body sensor network (BSN) technology plays a vital role in the healthcare system where lightweight wireless and low-powered sensor nodes used for monitoring the patients. In this paper, we propose a healthcare system using IoT and BSN technology. This system includes various sensors like pulse rate sensor, temperature sensor, and blood pressure sensor. These sensors sense the parameters and send the data to the controller. According to the conditions, the buzzer will on as temperature exceeds the given range. It carries the sensed data to the LCD to display on it. At the same time, data send to doctors using the internet, so that they can give quick and proper solution in real-time. Many patients suffer because of not getting the timely and appropriate solution and help for their problem. Propo...