Digital transformation and machine learning to empower smart healthcare (original) (raw)
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Health Care Digitalization and Machine Learning
Digital health can be defined as the use of wearable technology, connected software solutions, artificial intelligence, machine learning, and big data is for empowering and portioning patients to achieve healthcare through informed healthcare choices and available resources. Increased access to health knowledge results in fewer medical visits and improves healthcare as providers deliver high-quality service and outcomes when they can target individual needs. Online Consultation through Digital has grown during pandemics and has become an essential aspect of our life. As costs go down, more patients can access services thereby increasing the reach of healthcare facilities even in areas lacking adequate medical staff which eventually results in digital health care platforms and online consultation. Machine learning is helping digital health care to optimize the results of the search in finding better doctors, treatment, hospitals, and consultations. Certain ML aspects help digital platforms to predict the disease and various other health parameters. Our paper is a review of the digitalization of health care and how machine learning is helping in this context.
—The paradigm change from delayed interventional to Predictive, Preventive and Personalized Medicine is a leading global challenge in the 21st century. Ubiquitous convergence of mobile applications, new intelligent sensors and machine learning methods make possible creation of new generation personalized automatic healthcare monitoring and pathologies detection systems. These systems will help to make platforms for more effective treatments tailored to the person, that is considered as the "medicine of the future". Implementation of these latest trends in medicine will make it possible to detect health deterioration remotely and will avoid millions of hospitalizations costing billions of dollars in the world every year. In the article the problem of cardiovascular diseases and the state of healthcare industry is described and general architecture of automatic system for heart pathologies detection is proposed.
Machine Learning and Digital Health
The clinical use of machine learning algorithms are transforming the digital health care industry in a rapid phase. These algorithms will be implemented in the clinical setting of the health care professionals by embedding them in smart devices through Internet of things and could be used by the patients also for managing chronic conditions of diseases. The exponential growth of investment in machine learning signals that research is accelerating, and more products may soon be targeting market entry.The paper addresses the applications and challenges of machine learning in Digital health care services.
E-Healthcare monitoring System for diagnosis of Heart Disease using Machine Learning
IRJET, 2022
Modern healthcare technology advancements have a tremendous impact on how well medical services are provided and how many lives are saved. Cardiovascular illness, often known as heart disease, is the most deadly and complex disease and is difficult to diagnose with the unaided eye. According to the WHO cardiovascular disease (CVD) claims millions lannually. According to Global Burden of Disease, CVDs account for about 24.8% of all deaths in India. The number of CVD-related deaths in India has increased annually, from 2.26million in 2019 to 4.77 million in 2020. Heart disease is typically diagnosed by a doctor after reviewing the patient's medical history, the results of their physical exam, and any troubling symptoms. However, the results of this method of diagnosis do not reliably identify patients who have cardiac disease. Additionally, it is costly and computationally challenging to assess. Most clinical diagnoses are made by doctors with training and experience. However, incidences of incorrect diagnosis and treatment continue to be reported. Numerous diagnostic tests are required of patients. Many times, not every test helps with a disease's accurate diagnosis. Machine learning algorithms can foresee this kind of illness. Utilizing various machine learning approaches to quickly analyze and diagnose HD is one of the project's primary research goals. Additionally, it appears that the machine learning prediction model is a crucial feature in this field of study. With the use of certain methods like Feature Selection, Record, Attribute Minimization, and Classification, this work intends to offer a new heart disease prediction model in this situation.
2014
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Artificial intelligence has significantly contributed in the evolution of medical informatics and biomedicine, providing a variety of tools available to be exploited, from rule-based expert systems and fuzzy logic to neural networks and genetic algorithms. Moreover, familiarizing people with smartphones and the constantly growing use of medical-related mobile applications enables complete and systematic monitoring of a series of chronic diseases both by health professionals and patients. In this work, we propose an integrated system for monitoring and early notification for patients suffering from heart diseases. CardioSmart365 consists of web applications, smartphone native applications, decision support systems, and web services that allow interaction and communication among end users: cardiologists, patients, and general...
An IoT-based Framework for Detecting Heart Conditions using Machine Learning
International Journal of Advanced Computer Science and Applications, 2023
A lot of diseases may be preventable if they can be analyzed or predicted from patient historical and family data. Predicting diagnosis depends on the gathered clinical and physiological data of patients. The more collected clinical and medical healthcare data, the more knowledge the medical support system may support. Hence, real monitoring clinical and healthcare data for patients is the trend of this decade based on Internet of Things technologies (IoT). IoT models facilitate human life by easily collecting clinical data remotely for recognizing diseases that are easily treatable if it is diagnosed early. This paper proposes a framework consisting of two models: (i) heart attack detection model (HADM); (ii) Electrocardiosignal ECG heartbeat multiclass-classification model (ECG-HMCM). Gridsearch is used to the hyperparameters optimization for different machine learning (ML) techniques. The used dataset in HADM consists of 1190 patients and 14 features. As the foundation of diagnosing cardiovascular disease is arrhythmia detection hence, we propose an ECG heartbeat multi-class classification model using MIT-BIH Arrhythmia and PTB Diagnostic ECG signals dataset which contains five categories with 109446 samples. K Nearest Neighbor (KNN) technique is applied to build ECG-HMCM in addition to the using of Gridsearch algorithm for hyperparameter optimization aiming to improve the accuracy of classification which achieved 97.5%. The proposed framework aims to facilitate human life by easily collecting clinical data remotely. The outcomes of the experiments show that the suggested framework works well in a practical setting.
Digital Wellness: A Smart Health Care System Using Machine Learning
International Journal of Advances in Computer Science and Technology, 2021
Nowadays,people face various diseases due to environmental condition and their living habits. So the prediction of disease at an earlier stage becomes an important task. But the accurate prediction based on symptoms becomes too difficult for the doctor. The correctprediction of disease is the most challenging task. To overcome this problem data mining plays an important role to predict the disease. Medical science has a large amount of data growth per year. Due to the increasing amount of data growth in the medicaland healthcare field the accurate analysis of medical data has been benefits from early patient care. With the help of disease data, data mining finds hidden pattern information in a huge amount of medical data. We proposed general disease prediction based on the symptoms of the patient. For the disease prediction, we use Convolutional neural network (CNN) machine learning algorithm for the accurate prediction of disease. For disease prediction required disease symptoms dataset. After general disease prediction, this system able to gives the risk associated with a general disease which is a lower risk of general disease or higher.
This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR- Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4,372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardi...
Application of IoT Framework for Prediction of Heart Disease using Machine Learning
International Journal on Recent and Innovation Trends in Computing and Communication
Prognosis of illnesses is a difficult problem these days throughout the globe. Elder people of twenty years and over are taken into consideration to be laid low with this sickness now a days. For example, human beings having HbA1c level more than 6.5% are diagnosed as infected with diabetic diseases. This paper uses IoT to evaluate threat factors which have been similar to heart diseases which are not treated properly. Diagnosis, prevention of heart disease may be done by use of machine learning (ML). There has been an extensive disconnect among Machine Learning architects, health care researchers, patients and physicians in their technology. This paper intends to perform an in-intensity evaluation on Machine Learning to make us of new advance technologies. Latest advances within the development of IoT implanted devices and other medicine delivery gadgets, disease diagnostic methods and other medical research have considerably helped human beings diagnosed heart diseases. New soft ...