BMC Health Services Research

Table 2 Evaluation of models through precision, recall and F1 scores for two and three zone classification

From: Predictis: an IoT and machine learning-based system to predict risk level of cardio-vascular diseases

Model	Two level-classification			Three level-classification
Model	Precision	Recall	F1 score	Precision	Recall	F1 score
KNN	0.877	0.881	0.878	0.697	0.690	0.690
Naive Bayes	0.839	0.845	0.840	0.685	0.679	0.681
Random Forest	0.851	0.847	0.849	0.645	0.645	0.644
Support Vector Machine	0.855	0.855	0.855	0.749	0.745	0.742
Gradient Boosting	0.840	0.836	0.838	0.624	0.625	0.625
SGD Classifier	0.837	0.792	0.800	0.564	0.588	0.563
XGB Classifier	0.867	0.865	0.866	0.778	0.777	0.777
MLP classifier	0.861	0.860	0.861	0.739	0.731	0.726
Decision Tree	0.838	0.840	0.839	0.571	0.553	0.554
AdaBoost	0.855	0.859	0.856	0.660	0.662	0.661

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ISSN: 1472-6963

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