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Top1. Introduction
E-Adoption of emerging technology in health care has become more popular to improve the patient cure during the pandemic era. It is the most cost-effective and reliable use of emerging technology in support of the health care industry for the enhancement of the patient’s survivability. Various diseases can be cured and treated if the symptoms are detected on time. Here the role of E-Adoption of emerging technology arises due to the requirement of an automated disease detection system. It will help in predicting the disease using emerging technology such as machine learning and deep learning in any automated detection system and reducing the dependability on the clinical expert.
Machine Learning Technology is a proven tool in healthcare analysis for predicting disease based on patient history. Predictive analysis is used to improve better treatment and forecasting of disease for patients struggling for decades. Most of the research has used pathology data or medical history to extract the relevant information using data mining techniques. Machine learning classifiers can understand complicated, hierarchical representations of data with less pre-processing and produce more correct results. Therefore, deep learning methods are becoming more popular in a variety of machine learning applications. In healthcare, machine learning models enabled us to see insight by processing and analysing huge medical information. Processing large volume of data is the main characteristics of big data analytics, therefore more attention has been paid to disease prediction from the attitude of massive data analysis. Several research have used autonomously determined characteristics from large information to improve classification accuracy in compared to previously selected features.
The goal is to use emerging technology to support patient care and advancement in the field of healthcare. The process of effectively diagnosing and identifying disease has been simplified only because of machine learning. The use of effective multiple machine learning algorithms in predictive analysis aids in the more accurate prediction of disease and treatment of patients (Jiang et.al. 2017; Gibbons et.al. 2019; Davenport et.al. 2019). Predicting the disease of patients with having long medical background can be done with help of daily data provided by healthcare, so this prediction can help medical experts in diagnosing their patients. Such type of decision-making techniques can utilize health care information so the researcher can train their system with these existing data. So, more research should be carried out to improve the quality of medication in healthcare.
In the realm of healthcare, soft computing technologies like machine learning and intelligent training and prediction schemes are used in different ways. Treatment and diagnosis of patients should be powered with the latest technologies to provide better judgment and prescription for diagnosis and health support (Goel et.al. 2018). Complex and large data in the field of healthcare can be used with machine learning methodology to assist patients, and can further be analyzed for clinical purposes. To provide better medical facilities and care doctors can utilize this information. Hence it can be concluded that in the field of healthcare, machine learning plays an important role to satisfy patients. K means clustering methods also can be utilized to solve medical problems with the assistance of the treatment history of a patient to forecast illnesses (Akben et.al. 2017).
Machine learning technologies are the key method to enhancing the concept of artificial intelligence. Synthetic Genius is developed to generate the ability of human cognitive functions. The increasing popularity of data analytics techniques and easiness in healthcare data availability, it's causing a paradigm shift in the advancement of healthcare. Many fashions for automatic analysis of several health diseases, like diabetes, COVID-19, and cancer, have these days been created (Gibbons et.al.2019). There have even been some mobile applications developed that can forecast the likelihood of disease and propose a diagnosis to a specific individual depending on their health circumstances. Effective early-stage diagnosis, on the other hand, is still regarded as a difficult task. Deep-learning models are now widely used by researchers to produce significantly better outcomes than models based on machine learning. (Davenport et.al. 2019).