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- W4285598088 abstract "The health condition of the patients needs to be monitored with immense care. Healthcare promotes good health, helps in monitoring the patient's health status, disease diagnosis, and its management along with recovery. Monitoring the health condition postdischarge or postoperation is required to ensure a speedy recovery. Healthcare services can benefit from technological advancements to ensure better service. Healthcare assisted with machine learning techniques plays a significant role in the effective diagnosis of ailments, monitoring patient's health condition, and extend support in taking suitable measures during abnormality. In the proposed work, we collect the patient's data using sensors and upload them to the cloud. The collected data are subjected to preprocessing followed by analysis. The patient's health is remotely monitored, and machine learning techniques are applied to foretell abnormalities in the patient's health condition. Existing remote monitoring systems are not flexible and, hence, may result in an increased number of false positives. We try to reduce unnecessary alerts via machine learning methods and data analytics. Essential attributes like pulse rate, blood pressure, temperature, gender, and cholesterol levels of the patient are taken into consideration while predicting the results. In the time of pandemics, like COVID-19 with the scarce availability of medical personnel and treatment resources, this prediction may help in taking appropriate measures at the earliest. We train the model with the Kaggle Heart Disease UCI data set and test the model with real-time patient data. We apply our model to k nearest neighbor (KNN) and Naïve Bayes algorithm. The KNN has performed well over the Naïve Bayes algorithm." @default.
- W4285598088 created "2022-07-16" @default.
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- W4285598088 date "2022-07-12" @default.
- W4285598088 modified "2023-09-27" @default.
- W4285598088 title "Machine Learning–Assisted Remote Patient Monitoring with Data Analytics" @default.
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- W4285598088 doi "https://doi.org/10.1002/9781119841937.ch1" @default.
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