Titlebook: Intelligent Healthcare; Applications of AI i Surbhi Bhatia,Ashutosh Kumar Dubey,Abhishek Kumar Book 2021 The Editor(s) (if applicable) and

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Intelligent Healthcare,tem is very simple; it is plugged in and sends the measured parameter reading to the cloud, and it is accessible to the recipient who has the ID and password. Medical professionals will obtain the reading and analyse it properly, and on the basis of that analysis decide whether the patient requires

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Big Data,search in this chapter on massive amounts of medical data decreases healthcare expenditures by integrating multimodal data from different outlets. Big data analysis utilizing the Advice Platform plays an essential part, offering a smart HRS strategy that provides visibility into how large data analy

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Implication of Statistical Methods on Patient Data: An Approach for Cancer Survivability Predictionerformance of the Cox proportional hazard model surpasses that of the 2D statistical comparison model of Kaplan-Meier estimator as it can incorporate multiple layers of clinical information which enriches the prediction outcome.

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Modelling Covid-19: Transmission Dynamics Using Machine Learning Techniques,and prediction. The prediction model obtained is based on the trend of the data with highest R.value and minimum residual. The model helps the authorities to make necessary arrangements during the emergency.

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An Innovative Pandemic Knowledgebase Using Machine Learning,amically. So, data analytics and analysis can be applied and utilized to achieve these above-mentioned goals. The objective of this chapter is to present meticulous data analysis for understanding the spreading pattern of this virus worldwide. At the outset, healthcare is given the highest priority.

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Reviewing Classification Methods on Health Care,as the best classifier. The experimental results obtained prove that the SVM (support vector machine) and the logistic regression classifier models gave the best and the most consistent results for all the chosen datasets.

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Predicting and Managing Glycemia Levels Using Advanced Time Series Forecasting Methods,s, intensive workouts, sleep, etc. takes place. The methodology comprises (1) analysis of ARIMAX and FbProphet time series forecasting techniques to predict future glycemic levels on the Nightscout dataset and (2) sheds some light over the interpretability of other parameters such as carbohydrates a

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Deep Learning in Healthcare, the method and its mindset. The key areas of translational bioinformatics, scientific imaging, continuous sensing, scientific computing sciences, and public health concentrate on the essential tasks of comprehensive research.

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