Abstract:
Data analytics has been at the centre of complex problems related to healthcare. In fact, its usage has the potential to improve the ability of predicting patients' outcomes and assess their health status based not only on their medical records but on their lifestyle as well.
This master’s thesis focuses on people’s personal health perception, i.e. how an individual assesses his or her own personal health. In particular, with the use of the Italian PASSI dataset, it presents a deeper exploration of this concept with a focus on the population’s health perception in advanced age. The aim is to gain insights on which factors influence the subjective health perception by analysing the data collected through the biannual survey.
To start with, the thesis provides an overview of the literature related to subjective health perception around the world, and then it continues by introducing and describing the PASSI dataset, its objectives, and methodology.
Furthermore, there is an explanation of the data analytics techniques used for the analysis. It goes from descriptive statistics and exploratory data analysis to variables distributions and statistical models. The final goal is to identify patterns and present the results. The final predictive chosen model will be able to evaluate the general health status and will unravel the impact of lifestyle factors such as diet and physical activity, smoking and alcohol drinking on health outcomes and how those have an impact on the health perception and the subsequent development of diseases.
