From: Predicting high blood pressure using machine learning models in low- and middle-income countries
Country | Test | Train | Country | Test | Train |
---|---|---|---|---|---|
Afghanistan | 727 | 2876 | Liberia | 47 | 228 |
Algeria | 1166 | 4939 | Libya | 616 | 2587 |
Armenia | 348 | 1475 | Madagascar | 1021 | 4169 |
Azerbaijan | 482 | 2013 | Malawi | 630 | 2590 |
Bahamas | 242 | 1113 | Maldives | 279 | 1106 |
Bangladesh | 1471 | 5964 | Mali | 204 | 834 |
Barbados | 56 | 242 | Micronesia | 235 | 927 |
Belarus | 979 | 3916 | Moldova | 696 | 2971 |
Benin | 921 | 3528 | Mongolia | 992 | 4319 |
Bhutan | 1088 | 4121 | Mozambique | 535 | 2106 |
Botswana | 700 | 2755 | Myanmar | 1425 | 5542 |
Chad | 357 | 1306 | Namibia | 635 | 2556 |
Comoros | 856 | 3470 | Nauru | 196 | 808 |
Ecuador | 893 | 3521 | Nepal | 1116 | 4319 |
Eritrea | 1271 | 4879 | Niger | 445 | 1785 |
Eswatini | 546 | 2200 | Niue | 153 | 622 |
Ethiopia | 1864 | 7319 | Palau | 300 | 1086 |
Fiji | 493 | 1982 | Palestine | 1286 | 5184 |
Gabon | 453 | 1854 | Qatar | 431 | 1696 |
Gambia | 667 | 2484 | Rwanda | 1385 | 5182 |
Georgia | 804 | 2984 | Samoa | 312 | 1186 |
Ghana | 507 | 1978 | Tanzania | 1011 | 4103 |
Grenada | 181 | 673 | Togo | 730 | 2857 |
Guinea | 449 | 1783 | Tokelau | 103 | 428 |
Guyana | 513 | 1965 | Tonga | 681 | 2923 |
Kiribati | 235 | 937 | Tuvalu | 216 | 798 |
Kyrgyzstan | 488 | 2034 | Uganda | 727 | 2867 |
Lesotho | 351 | 1385 | Vanuatu | 844 | 3525 |
Zambia | 661 | 2654 | Â | Â | Â |