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Journal of Big Data

Table 8 AUC Results for Train_CV

From: The effects of class rarity on the evaluation of supervised healthcare fraud detection models

Learner

Ratio

200

400

1000

All

(a) Part B

GBT

[Full]

0.75982

0.78328

0.79120

0.79569

[1:99]

0.76740

0.79520

0.80378

0.80373

[10:90]

0.76032

0.79377

0.81847

0.82064

[25:75]

0.74964

0.78624

0.81464

0.81948

[35:65]

0.73271

0.77326

0.80600

0.81434

[50:50]

0.71530

0.75244

0.79563

0.80499

LR

[Full]

0.77162

0.78921

0.80019

0.80516

[1:99]

0.78282

0.79295

0.81119

0.81238

[10:90]

0.77752

0.79680

0.81465

0.81881

[25:75]

0.76797

0.79746

0.81507

0.81686

[35:65]

0.75771

0.79061

0.81336

0.81806

[50:50]

0.73414

0.78012

0.80964

0.81415

RF

[Full]

0.71510

0.73806

0.78110

0.79604

[1:99]

0.74846

0.77197

0.80579

0.81586

[10:90]

0.76661

0.79117

0.81933

0.83012

[25:75]

0.76031

0.79187

0.81641

0.82703

[35:65]

0.75699

0.78061

0.81299

0.82156

[50:50]

0.74994

0.77298

0.80448

0.81496

Learner

Ratio

100

200

400

All

(b) Part D

GBT

[Full]

0.68101

0.71044

0.73932

0.74851

[1:99]

0.68871

0.70412

0.74731

0.75727

[10:90]

0.66033

0.69299

0.74381

0.76756

[25:75]

0.65692

0.67700

0.73008

0.76538

[35:65]

0.63219

0.66694

0.71228

0.75996

[50:50]

0.62040

0.65461

0.70773

0.74506

LR

[Full]

0.72516

0.75436

0.77369

0.78164

[1:99]

0.71200

0.75396

0.77575

0.78486

[10:90]

0.71031

0.75129

0.77481

0.78657

[25:75]

0.70331

0.73115

0.77009

0.78540

[35:65]

0.67880

0.72835

0.76340

0.78216

[50:50]

0.67158

0.70696

0.74834

0.77557

RF

[Full]

0.62721

0.63364

0.66818

0.70888

[1:99]

0.67627

0.68215

0.70816

0.73706

[10:90]

0.67777

0.69735

0.73538

0.75857

[25:75]

0.67634

0.69916

0.72832

0.75838

[35:65]

0.65126

0.68992

0.72510

0.74904

[50:50]

0.64951

0.68343

0.70771

0.74088

Learner

Ratio

100

200

400

All

(c) DMEPOS

GBT

[Full]

0.67203

0.68827

0.72125

0.73129

[1:99]

0.66654

0.68611

0.72516

0.73591

[10:90]

0.65411

0.68073

0.72241

0.73777

[25:75]

0.64571

0.66342

0.71327

0.73389

[35:65]

0.61468

0.64740

0.70118

0.72090

[50:50]

0.60699

0.63259

0.68728

0.70598

LR

[Full]

0.68783

0.70311

0.73615

0.74063

[1:99]

0.68960

0.69565

0.73853

0.74085

[10:90]

0.67423

0.69604

0.73498

0.74421

[25:75]

0.66667

0.68769

0.72912

0.73715

[35:65]

0.65088

0.68259

0.72463

0.73488

[50:50]

0.64590

0.66432

0.71445

0.72225

RF

[Full]

0.61229

0.64745

0.69381

0.70756

[1:99]

0.64896

0.66998

0.70598

0.72245

[10:90]

0.65829

0.67671

0.72066

0.73767

[25:75]

0.65636

0.67337

0.71790

0.72889

[35:65]

0.64239

0.67054

0.71756

0.72390

[50:50]

0.63938

0.66152

0.70306

0.72379

Learner

Ratio

100

200

All

 

(d) Combined

GBT

[Full]

0.73906

0.76623

0.79047

 

[1:99]

0.73626

0.78562

0.80373

 

[10:90]

0.72482

0.76730

0.81675

 

[25:75]

0.68806

0.75833

0.80405

 

[35:65]

0.68275

0.74855

0.79127

 

[50:50]

0.65960

0.72675

0.77587

 

LR

[Full]

0.74260

0.80043

0.81554

 

[1:99]

0.73814

0.80060

0.82011

 

[10:90]

0.72508

0.78653

0.81868

 

[25:75]

0.69117

0.77479

0.81553

 

[35:65]

0.67940

0.76854

0.80998

 

[50:50]

0.67567

0.74588

0.79415

 

RF

[Full]

0.64769

0.71098

0.79383

 

[1:99]

0.71813

0.76663

0.81515

 

[10:90]

0.73110

0.79011

0.82793

 

[25:75]

0.74162

0.77822

0.81503

 

[35:65]

0.72834

0.76699

0.80619

 

[50:50]

0.71446

0.76228

0.79546

 
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