Random Forest Algorithm for Real-Time Health Monitoring Throught Iot Data

The last decade made significant progress in the empire of orientation to the health monitoring systems after the invention of wearable devices, simplifying health monitoring on a daily base. Devices combining “Internet-of-Things” and “Machine learning” technologies provide a solution that is persistent, objective, and feasible for remote monitoring, thereby facilitating ambient assisted living. This study aims to utilize a Random Forest machine learning algorithm to address clinical issues after achieving results on ML computations implemented on a dataset. In the subsequent tests, certain data will be collected, e.g., vital signs and body temperature heart rate, blood pressure, etc, utilizing IoT implemented devices. Health tracker devices combined with a series of body sensors revolutionize the system of living and health care regarding patient activity. Smartwatches bring the sensation of being one of the principal devices that often provide information regarding the step counter, heart rate, and sleep pattern, which is also crucial. The combination of the intelligent system of SPO2, heart rate, and body temperature sensors is often integrated with smartwatches find application, collecting the data and transferring it to the cloud for further analysis achieved by ML algorithm and Random Forest Machine Learning algorithm utilization. The testing phase pursues the notion, aiming to identify the level of accuracy in clinical issue detection, which confirms the system demonstrated in the work is efficient for remote monitoring

Implementing electronic scales to support standardized phenotypic data collection - the case of the Scale for the Assessment and Rating of Ataxia (SARA)

The main objective of this doctoral thesis was to facilitate the integration of the semantics required to automatically interpret collections of standardized clinical data. In order to address the objective, we combined the best performances from clinical archetypes, guidelines and ontologies for developing an electronic prototype for the Scale of the Assessment and Rating of Ataxia (SARA), broadly used in neurology. A scaled-down version of the Human Phenotype Ontology was automatically extracted and used as backbone to normalize the content of the SARA through clinical archetypes. The knowledge required to exploit reasoning on the SARA data was modeled as separate information-processing units interconnected via the defined archetypes. Based on this approach, we implemented a prototype named SARA Management System, to be used for both the assessment of cerebellar syndrome and the production of a clinical synopsis. For validation purposes, we used recorded SARA data from 28 anonymous subjects affected by SCA36. Our results reveal a substantial degree of agreement between the results achieved by the prototype and human experts, confirming that the combination of archetypes, ontologies and guidelines is a good solution to automate the extraction of relevant phenotypic knowledge from plain scores of rating scales