Abstract
Dehydration and overhydration can help to improve medical implications on health. Therefore, it is vital to track the hydration level (HL) specifically in children, the elderly and patients with underlying medical conditions such as diabetes. Most of the current approaches to estimate the hydration level are not sufficient and require more in-depth research. Therefore, in this paper, we used the non-invasive wearable sensor for collecting the skin conductance data and employed different machine learning algorithms based on feature engineering to predict the hydration level of the human body in different body postures. The comparative experimental results demonstrated that the random forest with an accuracy of 91.3% achieved better performance as compared to other machine learning algorithms to predict the hydration state of human body. This study paves a way for further investigation in non-invasive proactive skin hydration detection which can help in the diagnosis of serious health conditions.
Highlights
Machine learning and sensing technologies emerged as key players for advanced healthcare systems [1]
The results are generated by using python 3.8 version, using different features such as mean, standard deviation, square root, percentile, minimum, kurtosis and skewness. These results are calculated from the data of skin hydration which is collected via a non-invasive method
The experimental results show that the Random forest achieve better performance as compared to other algorithms
Summary
Machine learning and sensing technologies emerged as key players for advanced healthcare systems [1]. They are expected as intelligent, autonomous and ubiquitous decision making systems for the diagnosis and treatment of diseases. The intelligence required for such decision making can be gathered by the application of machine learning on the healthcare data comprising patients’ medical history, medical test reports, logs of monitoring devices, etc. The use of smart sensor-based healthcare devices have increased the data generation manifolds. Such devices, including wearable fitness bands, implanted chips, auto-injectors, defibrillators, etc. Machine learning can leverage from this data for the provision of seamless healthcare services in an autonomous manner [2]
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