## 4 Case Study of Cardio-Vascular Disease Using Fuzzy Logic  
This section demonstrates a typical case study of predicting the risk of cardiovascular disease with various learning algorithms including Fuzzy Logic [65; 66; 67; 68; 69]. This real data-based experiment was performed to consolidate and verify Fuzzy Logic and CNN and various other popular soft computing solutions in heart disease monitoring for patients in a Southern Asian country.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**No.** & **Authors** & **Year** & **Methodology** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline
12 & [PERSON] et al. & 2019 & X2-DNN & 93.33\% & 0.85 & 100 \\ \hline
13 & [PERSON] et al. & 2019 & HRFLM & 88.7\% & 0.92 & 0.82 \\ \hline
14 & [PERSON] et al. & 2019 & HOBDBNN & 99.03\% & 0.9983 & 0.9904 \\ \hline
15 & [PERSON] et al. & 2019 & DT, MLP, LR, SVM, NB & 84\% & NA & NA \\
16 & [PERSON] et al. & 2019 & SVM, DT, Random Forest, NB & 84.85\% & NA & NA \\ \hline
17 & [PERSON] et al. & 2019 & ANN, SVM, GA, DT, KNN & 89\% & 0.83 & 0.84 \\ \hline
18 & [PERSON] et al. & 2020 & HKNN, ant colony optimization & 99.2\% & 0.97 & 0.98 \\ \hline
19 & [PERSON] et al. & 2020 & CHI-PCA and RF & 99.4\% & 1.00 & 0.98 \\ \hline
20 & [PERSON] et al. & 2020 & NNDT & 99.2\% & 0.98 & 0.99 \\ \hline
21 & [PERSON] et al. & 2021 & ANN, SVM and DT & 92.56\% & 0.86 & 1.00 \\ \hline
22 & [PERSON] et al. & 2015 & Feed-forward multi-layer perception and SVM & 87.5\% & 0.84 & 0.89 \\ \hline
23 & [PERSON] et al. & 2016 & PSO and Feedforward Backpropagation & 91.94\% & 0.91 & 0.93 \\ \hline
24 & [PERSON] et al. & 2016 & Fuzzy Logic & 81.82\% & 0.78 & 0.84 \\ \hline
25 & [PERSON] et al. & 2017 & GA-RFNN & 97.78\% & 0.97 & 0.95 \\ \hline
26 & [PERSON] et al. & 2017 & SVM and MLP & 97\% & 97.4 & 98.7 \\ \hline
27 & [PERSON] et al. & 2017 & Probabilistic principal component analysis & 91.30\% & 1.00 & 0.50 \\ \hline
28 & [PERSON] et al. & 2017 & Fuzzy Logic & 93.33\% & NA & NA \\ \hline
29 & [PERSON] et al. & 2018 & KNN & 85\% & NA & NA \\ \hline
30 & [PERSON] et al. & 2018 & Fuzzy Logic & 85\% & NA & NA \\ \hline
32 & [PERSON] et al. & 2018 & Fuzzy Logic & 98\% & NA & NA \\ \hline
33 & [PERSON] et al. & 2019 & Imperialist Competitive Algorithm (ICA) and K-nearest neighbor & 88\% & 0.94 & 0.83 \\ \hline
34 & [PERSON] et al. & 2021 & Fuzzy Logic and Random Forest & 94.55\% & 0.95 & 0.95 \\ \hline
35 & [PERSON] et al. & 2015 & Fuzzy logic & 94.55\% & 0.63 & 0.65 \\ \hline
36 & [PERSON]. et al. & 2016 & Fuzzy analytic hierarchy process & 91.10\% & 1.00 & 0.84 \\ \hline
37 & [PERSON] et al. & 2017 & ANN and LSTM & NA & 0.26 & 0.26 \\ \hline
38 & [PERSON] et al. & 2018 & Multi fractal point process & 79\% & 0.90 & 0.67 \\ \hline
39 & [PERSON] et al. & 2018 & OR, DRM & 84.84\% & NA & NA \\ \hline
40 & [PERSON] et al. & 2019 & MT-DWNN & 93\% & 0.93 & 0.90 \\ \hline
41 & [PERSON] et al. & 2019 & HNCL, AMLN & 97.80\% & 0.95 & 1.00 \\ \hline
42 & [PERSON] et al. & 2015 & MLM & 91.5\% & 0.88 & 0.94 \\ \hline
43 & [PERSON] et al. & 2017 & NN-GA & 93.85\% & 0.97 & 0.92 \\ \hline
44 & [PERSON] et al. & 2018 & ANN 90.9\% & NA & NA & \\ \hline
45 & [PERSON] et al. & 2018 & Machine learning & 87\% & 0.82 & 0.92 \\ \hline
46 & [PERSON] et al. & 2018 & PSO-SVM & 84.36\% & NA & NA \\ \hline
47 & [PERSON] et al. & 2019 & PSO, 1-DCNN-SVM & 98.25\% & 0.98 & 0.98 \\ \hline
48 & [PERSON] al. & 2019 & Fuzzy logic & 99.3\% & 98.3 & 98.2 \\ \hline
49 & [PERSON] et al. & 2021 & CNN, BiLSTM & 99.30\% & 0.99 & 0.99 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._