Towards a precision medicine Solution for optimal pediatric Laparoscopy: An exploratory data analysis for features Selections

Over the past years, healthcare methodologies were commonly applied to all patients affected by the same pathology as an overall treatment. However, recent scientific works were mainly aimed at personalized care, according to the patient's clinical conditions and morphological characteristics. Pediatric surgery was also involved in this process, although laparoscopy has been set aside so far. In this regard, the physical characteristic that correlates intra-abdominal pressure and intra-abdominal volume (i.e., abdominal com-pliance) was always studied as a unique curve. This work aims to propose an alternative approach to treat laparoscopy surgery and abdominal compli-ance for pediatric individuals, which is based on personalized medicine. We perform an exploratory data analysis considering a real dataset, in order to study the data model (i.e., descriptive univariate and multivariate analysis) and the predictors (i.e., variance thresholding, Shapiro-Wilk test, Pearson's correlation coefficient) that will allow physicians to build clusters of pediatric individuals. Data analysis proves that there are strong correlations among features referring to thorax circumference and xifo-bisialic lenght with the sum of thorax and pelvis volumes. This leads to identify the optimal number of clusters through the Elbow method.