An Accurate Heart Disease Prognosis Using Machine Intelligence and IoMT

Table 5

Results of the proposed method in comparison with other methods based on numerical resources in Cleveland dataset.

Method	Accuracy	Precision	Recall	Specificity	F-score
Logistic regression [8]	83.3	—	86.3	82.3	—
K-neighbors [8]	84.8	—	85.0	77.7	—
SVM [8]	83.2	—	78.2	78.7	—
Random forest [8]	80.3	—	78.2	78.7	—
Decision tree [8]	82.3	—	78.5	78.9	—
DL [8]	94.2	—	82.3	83.1	—
K-nearest neighbor [5]	75.73	—	—	—	—
Decision trees [5]	72.45	—	—	—	—
Random forest [5]	75.73	—	—	—	—
Multilayer perceptron [5]	67.54	—	—	—	—
Naïve Bayes [5]	76.26	—	—	—	—
Linear support vector machine [5]	77.73	—	—	—	—
Faster R-CNN with SE-ResNeXt-101 [4]	98.00	96.16	98.47	96.02	97.58
Proposed method	98.7	96.61	99.18	96.65	98.48