08/11/2022
Περίληψη
Extracting useful knowledge from proper data analysis is a very
challenging task for efficient and timely decision-making. To achieve...
this, there exist a plethora of machine learning (ML)
algorithms, while, especially in healthcare, this
complexity increases due to the domain’s requirements
for analytics-based risk predictions. This manuscript
proposes a data analysis mechanism experimented in diverse
healthcare scenarios, towards constructing a catalogue of the
most efficient ML algorithms to be used depending on the healthcare scenario’s requirements and datasets, for efficiently predicting the onset of a disease. To this context, seven (7) different ML algorithms (Naïve Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, Neural Networks, Stochastic Gradient Descent) have been executed on top of diverse healthcare scenarios (stroke, COVID-19, diabetes, breast cancer, kidney disease, heart failure). Based on a variety of performance metrics (accuracy, recall, precision,
F1-score, specificity, confusion matrix), it has been
identified that a sub-set of ML algorithms are more efficient
for timely predictions under specific healthcare scenarios,
and that is why the envisioned ML catalogue prioritizes the ML
algorithms to be used, depending on the scenarios’ nature and needed
metrics. Further evaluation must be performed considering additional
scenarios, involving state-of-the-art techniques (e.g., cloud deployment,
federated ML) for improving the mechanism’s efficiency.
Συγγραφείς
Argyro Mavrogiorgou, Athanasios Kiourtis, Spyridon Kleftakis, Konstantinos Mavrogiorgos, Nikolaos Zafeiropoulos, Dimosthenis Kyriazis