Review Article
Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review
Table 4
Studies included in the other studies-section analysis and AI methods applied.
| | References | Method of data analysis | AI approaches |
| | Zhao et al. [99] | — | Deep neural networks | | Makimoto et al. [100] | SPSS | Deep neural networks | | Attia et al. [101] | — | Deep neural networks | | Sakli et al. [102] | — | Deep neural networks | | Elias et al. [103] | — | Deep neural networks | | Sangha et al. [105] | — | Deep neural networks | | Chang et al. [106] | — | Deep neural networks | | Liu et al. [107] | — | Machine learning (decision tree, K-means, back propagation neural network) | | Tutuko et al. [108] | — | Deep neural networks | | Xue et al. [109] | — | Decision tree | | Burgiardini et al. [110] | SPSS | Supervised machine learning (k nearest neighbor algorithm) | | Karimi et al. [111] | — | Deep neural networks | | Serra et al. [112] | — | Fuzzy logic | | Weissler et al. [113] | — | Natural language processing | | Baloch et al. [114] | — | Supervised machine learning | | Al Ramini et al. [115] | — | Machine learning | | Zhang et al. [116] | — | Machine learning | | Laughlin et al. [117] | SPSS | Machine learning | | Pirruccello et al. [119] | — | Deep learning | | Guo et al. [120] | SPSS | Machine learning | | Williams et al. [122] | Ingenuity | Machine learning | | Tsigalou et al. [124] | — | Machine learning | | Paragh et al. [125] | — | Deep neural networks (natural language processing- word2vec) | | Bermudez-Lopez et al. [126] | — | Machine learning (random forest analysis) | | Li et al. [127] | — | Human signaling networks, ClusterONE | | Yang et al. [128] | Cytoscape, MCODE | Machine learning | | Wang et al. [129] | DAVID, SPSS | Machine learning | | Tan et al. [130] | Cytoscape, MCODE | Machine learning | | Zhang et al. [131] | Cytoscape, MCODE | Machine learning | | Nai et al. [132] | Cytoscape, R package | Machine learning | | Huang et al. [134] | Cytoscape | Machine learning | | Yagi et al. [135] | GeneSpring | Machine learning | | Liu et al. [136] | Cluster 3.0 genes, Python | Machine learning | | Johno et al. [137] | — | Machine learning | | Wei and Quan [138] | DAVID | Machine learning | | Wang et al. [139] | Clustering, DAVID, Cytoscape, MCODE | Machine learning | | Wang et al. [140] | DAVID, R package, Cytoscape, MCODE | Machine learning | | Adela et al. [141] | — | Random forest analysis | | Canton et al. [144] | — | Deep neural networks | | Chen et al. [145] | — | Deep neural networks | | Jurtz et al. [146] | — | Deep learning | | Kigka et al. [147] | — | Machine learning | | Wang et al. [148] | — | Machine learning | | Xu et al. [149] | — | Machine learning | | Forrest et al. [150] | — | Machine learning | | Yang et al. [151] | — | Machine learning | | Sharma et al. [152] | — | Machine learning | | Chen et al. [153] | — | Machine learning | | Jones et al. [154] | — | Machine learning | | Jiang et al. [155] | — | Machine learning | | Ross et al. [156] | — | Machine learning | | Fan et al. [157] | — | Machine learning | | Cox et al. [158] | — | Machine learning | | Gao et al. [159] | — | Machine learning (logistic regression, random forest) | | Kumar and Priya [160] | — | Machine learning (support vector machine, kernel radial basis function) | | Park et al. [161] | — | Machine learning | | Dai et al. [162] | — | Supervised convolutional neural network | | Afzal et al. [163] | — | Natural language processing |
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