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  1. Best sensitivity: 0.605116 using {'dropout': 0.5, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 0, 'optimizer': 'adam'}
  2. Mean accuracy: 0.860955 (std: 0.026308); mean sensitivity: 0.553501 (std: 0.052534); mean specificity: 0.925983 (std: 0.029050); mean PPV: 0.635853 (std: 0.111911); mean NPV: 0.904926 (std: 0.013906) with: {'dropout': 0.25, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 306, 'optimizer': 'adam'}
  3. Mean accuracy: 0.863685 (std: 0.023203); mean sensitivity: 0.480392 (std: 0.069765); mean specificity: 0.945121 (std: 0.021972); mean PPV: 0.666979 (std: 0.126946); mean NPV: 0.893344 (std: 0.014550) with: {'dropout': 0.25, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 153, 'optimizer': 'adam'}
  4. Mean accuracy: 0.859591 (std: 0.021606); mean sensitivity: 0.496504 (std: 0.062215); mean specificity: 0.941358 (std: 0.033022); mean PPV: 0.674514 (std: 0.128638); mean NPV: 0.893272 (std: 0.028707) with: {'dropout': 0.25, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 0, 'optimizer': 'adam'}
  5. Mean accuracy: 0.860955 (std: 0.024441); mean sensitivity: 0.538663 (std: 0.055203); mean specificity: 0.929292 (std: 0.028218); mean PPV: 0.640411 (std: 0.116842); mean NPV: 0.902528 (std: 0.013378) with: {'dropout': 0.25, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 306, 'optimizer': 'adam'}
  6. Mean accuracy: 0.860955 (std: 0.024830); mean sensitivity: 0.550661 (std: 0.051139); mean specificity: 0.927446 (std: 0.032330); mean PPV: 0.647026 (std: 0.116562); mean NPV: 0.904065 (std: 0.016535) with: {'dropout': 0.25, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 153, 'optimizer': 'adam'}
  7. Mean accuracy: 0.860197 (std: 0.023376); mean sensitivity: 0.517156 (std: 0.030065); mean specificity: 0.934912 (std: 0.022299); mean PPV: 0.646025 (std: 0.085789); mean NPV: 0.897358 (std: 0.021842) with: {'dropout': 0.25, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 0, 'optimizer': 'adam'}
  8. Mean accuracy: 0.859439 (std: 0.028561); mean sensitivity: 0.533024 (std: 0.046149); mean specificity: 0.929214 (std: 0.025485); mean PPV: 0.633497 (std: 0.099552); mean NPV: 0.900491 (std: 0.018871) with: {'dropout': 0.25, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 306, 'optimizer': 'adam'}
  9. Mean accuracy: 0.855042 (std: 0.028501); mean sensitivity: 0.524171 (std: 0.022603); mean specificity: 0.928761 (std: 0.032784); mean PPV: 0.637742 (std: 0.125607); mean NPV: 0.897269 (std: 0.025806) with: {'dropout': 0.25, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 153, 'optimizer': 'adam'}
  10. Mean accuracy: 0.855648 (std: 0.022793); mean sensitivity: 0.525829 (std: 0.054450); mean specificity: 0.928050 (std: 0.022944); mean PPV: 0.625193 (std: 0.067232); mean NPV: 0.897951 (std: 0.025107) with: {'dropout': 0.25, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 0, 'optimizer': 'adam'}
  11. Mean accuracy: 0.854132 (std: 0.036621); mean sensitivity: 0.514712 (std: 0.091673); mean specificity: 0.923736 (std: 0.043100); mean PPV: 0.633844 (std: 0.160552); mean NPV: 0.898659 (std: 0.011228) with: {'dropout': 0.25, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 306, 'optimizer': 'adam'}
  12. Mean accuracy: 0.861410 (std: 0.032242); mean sensitivity: 0.573256 (std: 0.089236); mean specificity: 0.920216 (std: 0.038501); mean PPV: 0.636612 (std: 0.148393); mean NPV: 0.910145 (std: 0.006953) with: {'dropout': 0.25, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 153, 'optimizer': 'adam'}
  13. Mean accuracy: 0.864291 (std: 0.029300); mean sensitivity: 0.565351 (std: 0.068143); mean specificity: 0.927906 (std: 0.023935); mean PPV: 0.637822 (std: 0.111570); mean NPV: 0.906984 (std: 0.018265) with: {'dropout': 0.25, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 0, 'optimizer': 'adam'}
  14. Mean accuracy: 0.863230 (std: 0.029304); mean sensitivity: 0.544527 (std: 0.049099); mean specificity: 0.932387 (std: 0.026759); mean PPV: 0.647046 (std: 0.136805); mean NPV: 0.902891 (std: 0.019206) with: {'dropout': 0.25, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 306, 'optimizer': 'adam'}
  15. Mean accuracy: 0.864898 (std: 0.029930); mean sensitivity: 0.554821 (std: 0.089973); mean specificity: 0.931405 (std: 0.019711); mean PPV: 0.637690 (std: 0.125362); mean NPV: 0.905291 (std: 0.021264) with: {'dropout': 0.25, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 153, 'optimizer': 'adam'}
  16. Mean accuracy: 0.864291 (std: 0.027895); mean sensitivity: 0.554291 (std: 0.048050); mean specificity: 0.930145 (std: 0.027614); mean PPV: 0.648753 (std: 0.107560); mean NPV: 0.905039 (std: 0.016185) with: {'dropout': 0.25, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 0, 'optimizer': 'adam'}
  17. Mean accuracy: 0.862775 (std: 0.028509); mean sensitivity: 0.530974 (std: 0.064216); mean specificity: 0.934900 (std: 0.020829); mean PPV: 0.643069 (std: 0.126138); mean NPV: 0.900389 (std: 0.021949) with: {'dropout': 0.25, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 306, 'optimizer': 'adam'}
  18. Mean accuracy: 0.859742 (std: 0.030101); mean sensitivity: 0.592708 (std: 0.055309); mean specificity: 0.915790 (std: 0.030920); mean PPV: 0.618984 (std: 0.107202); mean NPV: 0.911563 (std: 0.014517) with: {'dropout': 0.25, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 153, 'optimizer': 'adam'}
  19. Mean accuracy: 0.857316 (std: 0.024670); mean sensitivity: 0.562744 (std: 0.020781); mean specificity: 0.922717 (std: 0.030271); mean PPV: 0.627486 (std: 0.112349); mean NPV: 0.904502 (std: 0.022660) with: {'dropout': 0.25, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 0, 'optimizer': 'adam'}
  20. Mean accuracy: 0.860349 (std: 0.025812); mean sensitivity: 0.543477 (std: 0.088006); mean specificity: 0.928148 (std: 0.015241); mean PPV: 0.619743 (std: 0.105527); mean NPV: 0.902906 (std: 0.020810) with: {'dropout': 0.25, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 306, 'optimizer': 'adam'}
  21. Mean accuracy: 0.860349 (std: 0.030899); mean sensitivity: 0.581288 (std: 0.060459); mean specificity: 0.919596 (std: 0.028154); mean PPV: 0.622164 (std: 0.117859); mean NPV: 0.909369 (std: 0.016929) with: {'dropout': 0.25, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 153, 'optimizer': 'adam'}
  22. Mean accuracy: 0.861713 (std: 0.032590); mean sensitivity: 0.579375 (std: 0.096224); mean specificity: 0.920127 (std: 0.029477); mean PPV: 0.621482 (std: 0.118814); mean NPV: 0.910212 (std: 0.016166) with: {'dropout': 0.25, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 0, 'optimizer': 'adam'}
  23. Mean accuracy: 0.863230 (std: 0.033487); mean sensitivity: 0.583778 (std: 0.059270); mean specificity: 0.925138 (std: 0.029221); mean PPV: 0.636683 (std: 0.140936); mean NPV: 0.908998 (std: 0.023702) with: {'dropout': 0.25, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 306, 'optimizer': 'adam'}
  24. Mean accuracy: 0.863381 (std: 0.029692); mean sensitivity: 0.544653 (std: 0.049672); mean specificity: 0.933846 (std: 0.023980); mean PPV: 0.647778 (std: 0.130241); mean NPV: 0.902107 (std: 0.024489) with: {'dropout': 0.25, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 153, 'optimizer': 'adam'}
  25. Mean accuracy: 0.862926 (std: 0.026802); mean sensitivity: 0.545914 (std: 0.028199); mean specificity: 0.932483 (std: 0.025953); mean PPV: 0.647802 (std: 0.126381); mean NPV: 0.902628 (std: 0.020856) with: {'dropout': 0.25, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 0, 'optimizer': 'adam'}
  26. Mean accuracy: 0.865353 (std: 0.031092); mean sensitivity: 0.535213 (std: 0.082298); mean specificity: 0.936603 (std: 0.022705); mean PPV: 0.650454 (std: 0.142757); mean NPV: 0.901860 (std: 0.021160) with: {'dropout': 0.25, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 306, 'optimizer': 'adam'}
  27. Mean accuracy: 0.867779 (std: 0.029796); mean sensitivity: 0.571101 (std: 0.044569); mean specificity: 0.931728 (std: 0.027809); mean PPV: 0.658833 (std: 0.129566); mean NPV: 0.907997 (std: 0.017960) with: {'dropout': 0.25, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 153, 'optimizer': 'adam'}
  28. Mean accuracy: 0.865353 (std: 0.024479); mean sensitivity: 0.572721 (std: 0.060744); mean specificity: 0.926963 (std: 0.027285); mean PPV: 0.642923 (std: 0.117584); mean NPV: 0.909262 (std: 0.011036) with: {'dropout': 0.25, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 0, 'optimizer': 'adam'}
  29. Mean accuracy: 0.860804 (std: 0.022713); mean sensitivity: 0.511073 (std: 0.028544); mean specificity: 0.937935 (std: 0.015322); mean PPV: 0.643685 (std: 0.096972); mean NPV: 0.896118 (std: 0.024335) with: {'dropout': 0.5, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 306, 'optimizer': 'adam'}
  30. Mean accuracy: 0.855497 (std: 0.023143); mean sensitivity: 0.564937 (std: 0.079164); mean specificity: 0.916918 (std: 0.036983); mean PPV: 0.623305 (std: 0.107711); mean NPV: 0.906681 (std: 0.015268) with: {'dropout': 0.5, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 153, 'optimizer': 'adam'}
  31. Mean accuracy: 0.858226 (std: 0.019716); mean sensitivity: 0.519227 (std: 0.102101); mean specificity: 0.929848 (std: 0.032415); mean PPV: 0.644170 (std: 0.086288); mean NPV: 0.899301 (std: 0.018275) with: {'dropout': 0.5, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 0, 'optimizer': 'adam'}
  32. Mean accuracy: 0.849128 (std: 0.023601); mean sensitivity: 0.519528 (std: 0.082612); mean specificity: 0.925531 (std: 0.045362); mean PPV: 0.632016 (std: 0.135093); mean NPV: 0.895483 (std: 0.033071) with: {'dropout': 0.5, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 306, 'optimizer': 'adam'}
  33. Mean accuracy: 0.858832 (std: 0.019316); mean sensitivity: 0.437026 (std: 0.122504); mean specificity: 0.946360 (std: 0.020759); mean PPV: 0.642047 (std: 0.063188); mean NPV: 0.887183 (std: 0.013373) with: {'dropout': 0.5, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 153, 'optimizer': 'adam'}
  34. Mean accuracy: 0.850341 (std: 0.020683); mean sensitivity: 0.541172 (std: 0.080064); mean specificity: 0.919353 (std: 0.033787); mean PPV: 0.611839 (std: 0.096291); mean NPV: 0.900064 (std: 0.027251) with: {'dropout': 0.5, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 0, 'optimizer': 'adam'}
  35. Mean accuracy: 0.852161 (std: 0.026002); mean sensitivity: 0.418757 (std: 0.036918); mean specificity: 0.950366 (std: 0.021481); mean PPV: 0.659640 (std: 0.109325); mean NPV: 0.879058 (std: 0.034483) with: {'dropout': 0.5, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 306, 'optimizer': 'adam'}
  36. Mean accuracy: 0.857316 (std: 0.024820); mean sensitivity: 0.538063 (std: 0.056150); mean specificity: 0.924848 (std: 0.029080); mean PPV: 0.626246 (std: 0.069700); mean NPV: 0.901534 (std: 0.016658) with: {'dropout': 0.5, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 153, 'optimizer': 'adam'}
  37. Mean accuracy: 0.851706 (std: 0.024183); mean sensitivity: 0.467292 (std: 0.072021); mean specificity: 0.935272 (std: 0.024912); mean PPV: 0.628742 (std: 0.068363); mean NPV: 0.888228 (std: 0.025087) with: {'dropout': 0.5, 'epochs': 10, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 0, 'optimizer': 'adam'}
  38. Mean accuracy: 0.858378 (std: 0.026612); mean sensitivity: 0.545786 (std: 0.022252); mean specificity: 0.928353 (std: 0.024024); mean PPV: 0.631004 (std: 0.113098); mean NPV: 0.901195 (std: 0.026237) with: {'dropout': 0.5, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 306, 'optimizer': 'adam'}
  39. Mean accuracy: 0.864139 (std: 0.027262); mean sensitivity: 0.565306 (std: 0.034893); mean specificity: 0.932958 (std: 0.034189); mean PPV: 0.663202 (std: 0.133622); mean NPV: 0.904623 (std: 0.029617) with: {'dropout': 0.5, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 153, 'optimizer': 'adam'}
  40. Mean accuracy: 0.860652 (std: 0.025088); mean sensitivity: 0.605116 (std: 0.044072); mean specificity: 0.916741 (std: 0.036435); mean PPV: 0.632439 (std: 0.118445); mean NPV: 0.913111 (std: 0.018521) with: {'dropout': 0.5, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 0, 'optimizer': 'adam'}
  41. Mean accuracy: 0.865201 (std: 0.025842); mean sensitivity: 0.530862 (std: 0.039438); mean specificity: 0.938115 (std: 0.022630); mean PPV: 0.658828 (std: 0.124255); mean NPV: 0.900556 (std: 0.020376) with: {'dropout': 0.5, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 306, 'optimizer': 'adam'}
  42. Mean accuracy: 0.864291 (std: 0.022942); mean sensitivity: 0.567798 (std: 0.019765); mean specificity: 0.929284 (std: 0.023341); mean PPV: 0.643384 (std: 0.109260); mean NPV: 0.906611 (std: 0.019623) with: {'dropout': 0.5, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 153, 'optimizer': 'adam'}
  43. Mean accuracy: 0.858074 (std: 0.022454); mean sensitivity: 0.557310 (std: 0.027171); mean specificity: 0.926081 (std: 0.027147); mean PPV: 0.631454 (std: 0.104658); mean NPV: 0.902996 (std: 0.026967) with: {'dropout': 0.5, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 0, 'optimizer': 'adam'}
  44. Mean accuracy: 0.858681 (std: 0.026288); mean sensitivity: 0.551443 (std: 0.028878); mean specificity: 0.925733 (std: 0.032432); mean PPV: 0.637005 (std: 0.126906); mean NPV: 0.903405 (std: 0.018680) with: {'dropout': 0.5, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 306, 'optimizer': 'adam'}
  45. Mean accuracy: 0.858074 (std: 0.027827); mean sensitivity: 0.546084 (std: 0.042159); mean specificity: 0.924531 (std: 0.031106); mean PPV: 0.632133 (std: 0.104304); mean NPV: 0.902751 (std: 0.016946) with: {'dropout': 0.5, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 153, 'optimizer': 'adam'}
  46. Mean accuracy: 0.859591 (std: 0.022306); mean sensitivity: 0.568294 (std: 0.036748); mean specificity: 0.923665 (std: 0.026583); mean PPV: 0.631038 (std: 0.091598); mean NPV: 0.905865 (std: 0.022170) with: {'dropout': 0.5, 'epochs': 20, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 0, 'optimizer': 'adam'}
  47. Mean accuracy: 0.855345 (std: 0.034539); mean sensitivity: 0.594252 (std: 0.004242); mean specificity: 0.913542 (std: 0.040745); mean PPV: 0.622392 (std: 0.138029); mean NPV: 0.909714 (std: 0.022169) with: {'dropout': 0.5, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 306, 'optimizer': 'adam'}
  48. Mean accuracy: 0.861107 (std: 0.031809); mean sensitivity: 0.589724 (std: 0.078713); mean specificity: 0.918297 (std: 0.026496); mean PPV: 0.618242 (std: 0.110516); mean NPV: 0.911103 (std: 0.018356) with: {'dropout': 0.5, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 153, 'optimizer': 'adam'}
  49. Mean accuracy: 0.865049 (std: 0.027650); mean sensitivity: 0.565453 (std: 0.037135); mean specificity: 0.929677 (std: 0.029977); mean PPV: 0.652450 (std: 0.132055); mean NPV: 0.906936 (std: 0.016100) with: {'dropout': 0.5, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 612, 'n_second_layer': 0, 'optimizer': 'adam'}
  50. Mean accuracy: 0.865049 (std: 0.027242); mean sensitivity: 0.523217 (std: 0.101393); mean specificity: 0.936544 (std: 0.022096); mean PPV: 0.645870 (std: 0.122831); mean NPV: 0.901157 (std: 0.015613) with: {'dropout': 0.5, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 306, 'optimizer': 'adam'}
  51. Mean accuracy: 0.863988 (std: 0.029906); mean sensitivity: 0.560801 (std: 0.043118); mean specificity: 0.931964 (std: 0.025760); mean PPV: 0.649016 (std: 0.128206); mean NPV: 0.904444 (std: 0.026585) with: {'dropout': 0.5, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 153, 'optimizer': 'adam'}
  52. Mean accuracy: 0.860652 (std: 0.026114); mean sensitivity: 0.547344 (std: 0.027059); mean specificity: 0.928827 (std: 0.024576); mean PPV: 0.636476 (std: 0.110463); mean NPV: 0.902701 (std: 0.020065) with: {'dropout': 0.5, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 306, 'n_second_layer': 0, 'optimizer': 'adam'}
  53. Mean accuracy: 0.862472 (std: 0.026148); mean sensitivity: 0.569409 (std: 0.037293); mean specificity: 0.925454 (std: 0.030223); mean PPV: 0.641178 (std: 0.120855); mean NPV: 0.907407 (std: 0.015561) with: {'dropout': 0.5, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 306, 'optimizer': 'adam'}
  54. Mean accuracy: 0.865201 (std: 0.026062); mean sensitivity: 0.515304 (std: 0.083157); mean specificity: 0.940410 (std: 0.015350); mean PPV: 0.651261 (std: 0.111663); mean NPV: 0.898567 (std: 0.021174) with: {'dropout': 0.5, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 153, 'optimizer': 'adam'}
  55. Mean accuracy: 0.864443 (std: 0.022883); mean sensitivity: 0.576100 (std: 0.062634); mean specificity: 0.926554 (std: 0.034664); mean PPV: 0.656651 (std: 0.120352); mean NPV: 0.908977 (std: 0.016915) with: {'dropout': 0.5, 'epochs': 30, 'learning_rate': 0.001, 'n_first_layer': 153, 'n_second_layer': 0, 'optimizer': 'adam'}
  56. --- Program execution time: 10366.80045747757 seconds ---
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