A Big Data Pipeline and Machine Learning for Uniform Semantic Representation of Data and Documents From IT Systems of the Italian Ministry of Justice

Martino, Beniamino Di; Cante, Luigi Colucci; D’Angelo, Salvatore; Esposito, Antonio; Graziano, Mariangela; Marulli, Fiammetta; Lupi, Pietro; Cataldi, Alessandra

doi:10.4018/ijghpc.301579

Cited by 12 publications

(1 citation statement)

References 17 publications

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“…Indeed, the reduced number of available samples in the original dataset, together with its complexity deriving from the considerable number of features describing each sample, required the application of different data preparation techniques, in order to avoid or at least reduce overfitting and imbalance problems. Similar issues have been addressed in other works, related to different dominions, such as in Di Martino et al (2021). In particular, since predicting the progression of the MS disease through a single indicator, namely the Expanded disability Status Scale (EDSS) described in Sect.…”

Section: Introductionmentioning

confidence: 73%

Machine learning techniques for prediction of multiple sclerosis progression

et al. 2022

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Patients afflicted by multiple sclerosis experience a relapsing-remitting course in about 85% of the cases. Furthermore, after a 10/15-year period their situation tends to worse, resulting in what is considered the second phase of multiple sclerosis. While treatments are now available to reduce the symptoms and slow down the progression of the disease, the administration of drugs must be adapted to the course of the disease, and predicting relapsing periods and the worsening of the symptoms can greatly improve the outcome of the treatment. For this reason, indicators such as the patient-reported outcome measures (PROMs) have been largely used to support early diagnosis and prediction of future relapsing periods in patients affected by multiple sclerosis. However, such indicators are insufficient, as the prediction they provide is often not accurate enough. In this paper, machine learning techniques have been applied to data obtained from clinical trial, in order to improve the prediction capabilities and provide doctors with an additional instrument to evaluate the clinical situation of patients. After the application of correlation indicators and the use of principal component analysis for the reduction of the dimensionality of the feature space, classification algorithms have been applied and compared, in order to identify the best suiting one for our purposes. After the application of re-balance algorithms, the accuracy of the machine learning-based prediction system reaches 79%, demonstrating the capability of the framework to correctly predict future progression of disability.

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Section: Introductionmentioning

confidence: 73%