Table 5 Classification results of each model based on different data sets [Mean (Standard Deviations)]

decision tree

Model 1

0.261(0.095)

0.885(0.104)

0.348(0.076)

0.671(0.205)

0.612(0.035)

0.626(0.052)

Model 2

0.448(0.145)

0.817(0.082)

0.514(0.147)

0.638(0.191)

0.664(0.052)

0.628(0.077)

Model 3

0.340(0.081)

0.805(0.103)

0.417(0.097)

0.570(0.171)

0.606(0.077)

0.570(0.077)

Model 4

0.412(0.127)

0.826(0.077)

0.489(0.137)

0.632(0.186)

0.655(0.049)

0.615(0.071)

Model 5

0.487(0.186)

0.776(0.108)

0.525(0.175)

0.608(0.206)

0.658(0.072)

0.658(0.104)

Model 6

0.410(0.209)

0.799(0.104)

0.451(0.192)

0.618(0.213)

0.63(0.079)

0.615(0.081)

SVM

Model 1

0.265(0.129)

0.932(0.048)

0.403(0.080)

0.738(0.123)

0.639(0.080)

0.547(0.049)

Model 2

0.210(0.182)

0.878(0.130)

0.272(0.163)

0.643(0.213)

0.606(0.062)

0.536(0.051)

Model 3

0.380(0.130)

0.831(0.058)

0.452(0.095)

0.614(0.107)

0.633(0.053)

0.548(0.049)

Model 4

0.417(0.123)

0.844(0.106)

0.506(0.091)

0.690(0.156)

0.667(0.073)

0.589(0.056)

Model 5

0.526(0.110)

0.846(0.077)

0.600(0.094)

0.721(0.124)

0.715(0.055)

0.606(0.065)

Model 6

0.583(0.124)

0.837(0.112)

0.644(0.106)

0.743(0.145)

0.736(0.074)

0.655(0.056)

random forest

Model 1

0.485(0.127)

0.802(0.074)

0.540(0.102)

0.639(0.116)

0.670(0.046)

0.680(0.054)

Model 2

0.443(0.118)

0.838(0.106)

0.523(0.110)

0.690(0.190)

0.673(0.048)

0.735(0.099)

Model 3

0.425(0.121)

0.772(0.095)

0.474(0.082)

0.579(0.123)

0.621(0.051)

0.679(0.045)

Model 4

0.414(0.130)

0.883(0.089)

0.511(0.118)

0.740(0.164)

0.688(0.027)

0.769(0.073)

Model 5

0.504(0.152)

0.866(0.111)

0.587(0.154)

0.743(0.218)

0.712(0.095)

0.780(0.074)

Model 6

0.514(0.109)

0.869(0.098)

0.597(0.105)

0.756(0.174)

0.718(0.062)

0.779(0.075)

neural network

Model 1

0.343(0.102)

0.876(0.090)

0.441(0.097)

0.688(0.178)

0.648(0.064)

0.676(0.060)

Model 2

0.354(0.214)

0.834(0.103)

0.420(0.177)

0.594(0.148)

0.642(0.054)

0.642(0.110)

Model 3

0.505(0.079)

0.771(0.109)

0.547(0.074)

0.625(0.141)

0.655(0.055)

0.660(0.062)

Model 4

0.378(0.182)

0.817(0.123)

0.429(0.137)

0.617(0.179)

0.624(0.045)

0.631(0.091)

Model 5

0.488(0.134)

0.827(0.105)

0.561(0.115)

0.686(0.137)

0.694(0.052)

0.741(0.065)

Model 6

0.61(0.108)

0.874(0.104)

0.686(0.095)

0.801(0.130)

0.767(0.081)

0.793(0.086)

naive bayes

Model 1

0.302(0.078)

0.867(0.092)

0.396(0.056)

0.663(0.191)

0.624(0.053)

0.650(0.067)

Model 2

0.279(0.134)

0.868(0.101)

0.371(0.132)

0.634(0.172)

0.624(0.087)

0.613(0.096)

Model 3

0.480(0.119)

0.858(0.068)

0.562(0.087)

0.718(0.096)

0.700(0.042)

0.767(0.064)

Model 4

0.279(0.134)

0.868(0.101)

0.371(0.132)

0.634(0.172)

0.624(0.087)

0.647(0.093)

Model 5

0.385(0.101)

0.907(0.065)

0.503(0.091)

0.770(0.096)

0.688(0.070)

0.761(0.078)

Model 6

0.385(0.101)

0.907(0.065)

0.503(0.091)

0.77(0.096)

0.688(0.070)

0.771(0.072)

logistic regression

Model 1

0.351(0.098)

0.838(0.104)

0.433(0.079)

0.630(0.167)

0.627(0.054)

0.667(0.065)

Model 2

0.391(0.183)

0.782(0.128)

0.444(0.155)

0.570(0.149)

0.627(0.054)

0.625(0.130)

Model 3

0.515(0.185)

0.667(0.129)

0.500(0.130)

0.529(0.106)

0.600(0.070)

0.602(0.100)

Model 4

0.505(0.154)

0.775(0.128)

0.541(0.115)

0.640(0.137)

0.664(0.053)

0.666(0.105)

Model 5

0.506(0.131)

0.690(0.150)

0.510(0.125)

0.565(0.180)

0.606(0.082)

0.615(0.083)

Model 6

0.494(0.122)

0.721(0.129)

0.515(0.124)

0.571(0.187)

0.621(0.075)

0.626(0.095)