Application Of Machine Learning In Healthcare: Analysis On MHEALTH Dataset

Abstract

The healthcare services in developed and developing countries are critically important. The use of machine learning techniques in healthcare industry has a vital importance and increases rapidly. The corporations in healthcare sector need to take advantage of the machine learning techniques to obtain valuable data that could later be used to diagnose diseases at much earlier stages. In this study, a research is conducted with the purpose of discovering further use of the machine learning techniques in healthcare sector. Research was conducted by analyzing a well-established dataset called MHEALTH, comprising body motion and vital signs recordings for ten volunteers of diverse profile while performing 12 physical activities. Dataset was analyzed using certain classification algorithms such as Multilayer Perceptron and Support Vector Machine, then results from these algorithms were compared to determine the most utile algorithm for analyzing such dataset. Study aims to determine irregularities using data from body motion and vital signs of volunteers, then these findings can be used either to diagnose particular diseases before they occur and avoid them. Results can also be used to monitor movements of ill or elderly people and observe whether they are doing any prohibited movements that would lead them to injuries or further illnesses.

Human activity recognition using wearable sensors, discriminant analysis, and long short-term memory-based neural structured learning

“…8 and 9 . To compare the proposed approach with other traditional approaches such as typical DBN, CNN, and RNN-based experiments 39 were done. However, they achieved a maximum mean recognition rate of 94%.…”

A collaborative empirical analysis on machine learning based disease prediction in health care system

“…Ichikawa et al in 2016 proposed remote medical check-up system based on a prediction model to save medical costs [19]. Attempts were made to identify abnormalities using data from participants' body motion and vital signs and utilised to either diagnose or avoid specific diseases [20]. On the other hand researchers predicted intensive care unit(ICU) mortality with an 80% accuracy [21].…”

Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive Noise

“…There are many real-life scenarios where the data is multidimensional and can potentially benefit if modeled using multidimensional graph signals. For example, in [24] multiple sensors are placed at multiple locations of the human body to monitor various vital signs and body motion, which could potentially be modeled using multifeature GSP by treating the vitals or the motions as the features and constructing the graph topology using the location of the sensors. In [25], a graph-theoretic model is given for multi-channel EEG signal, but the data from multi-channel are reduced to 1 dimension using synchronization likelihood.…”