Sensors, Vol. 22, Pages 8615: A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions

Sensors, Vol. 22, Pages 8615: A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions Sensors doi: 10.3390/s22228615 Authors: Argyro Mavrogiorgou Athanasios Kiourtis Spyridon Kleftakis Konstantinos Mavrogiorgos Nikolaos Zafeiropoulos Dimosthenis Kyriazis 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 ...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research