Chinese Medical Sciences Journal ›› 2022, Vol. 37 ›› Issue (3): 210-217.doi: 10.24920/004086
• Scientific Data Sharing and Reuse:Original Article • Previous Articles Next Articles
Chun Wang1, Qinxue Chang1, Xiaomeng Wang1, Keyun Wang1, He Wang2, Zhuang Cui1, *(), Changping Li1, *(
)
Received:
2022-03-21
Accepted:
2022-08-24
Published:
2022-09-30
Online:
2022-09-22
Contact:
Zhuang Cui,Changping Li
E-mail:zhuangcui417@126.com;changpingli417@126.com
Chun Wang, Qinxue Chang, Xiaomeng Wang, Keyun Wang, He Wang, Zhuang Cui, Changping Li. Prostate Cancer Risk Prediction and Online Calculation Based on Machine Learning Algorithm[J].Chinese Medical Sciences Journal, 2022, 37(3): 210-217.
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Table 1.
The variables from the dataset and abbreviations used in modeling and prediction of prostate cancer in this study"
Indicator | Abbreviation |
---|---|
Demographic information | |
Age | - |
Height | - |
Weight | - |
Body mass index | BMI |
Prostate indicators | |
Free prostate specific antigen | fPSA |
Total prostate specific antigen | tPSA |
Free prostate-specific antigen ratio | rPSA |
Volume of core-biopsy sampled tissue | CBV |
Offwhite fragile tissue in biopsied samples | OFT |
Transurethral resection prostate | TURP |
Presence of accompanying prostatic hyperplasia | - |
Volume of prostate | PV |
Serum enzymatic examination | |
Alkaline phosphatase | ALP |
Creatine kinase isoenzyme | CK_MB |
Lactic dehydrogenase | LDH |
Creatine kinase | CK |
Blood biochemical indicators | |
Creatinine | CR |
Albuminous | ALB |
Serum uric acid | UA |
Triglyceride | TG |
High density lipoprotein cholesterol | HDLC |
Low density lipoprotein cholesterol | LDLC |
Apolipoprotein A1 | ApoA1 |
Apolipoprotein B | ApoB |
Estimated glomerular filtration rate | eGFR |
Electrolyte indicators | |
Potassium | K |
Inorganic phosphorus | iP |
Sodium | Na |
Inorganic calcium | iCa |
Chloride | CL |
Table 2.
The confusion matrix of prediction results of four models"
Actual | Predicted | Row Total | |
---|---|---|---|
Non-PCa | PCa | ||
RF model | |||
Non-PCa | 149 | 3 | 152 |
PCa | 5 | 93 | 98 |
Column total | 154 | 96 | 250 |
SVM model | |||
Non-PCa | 141 | 26 | 167 |
PCa | 26 | 107 | 133 |
Column total | 167 | 133 | 300 |
BP model | |||
Non-PCa | 264 | 47 | 311 |
PCa | 19 | 270 | 289 |
Column total | 283 | 317 | 600 |
CNN model | |||
Non-PCa | 74 | 21 | 95 |
PCa | 35 | 71 | 106 |
Column total | 109 | 92 | 201 |
Table 3.
The comparison of AUC values of four models in test set"
Model | Accuracy (%) | Precision (%) | Recall (%) | AUC (95%CI) |
---|---|---|---|---|
RF | 96.80 | 96.88 | 94.90 | 0.975 (0.964, 0.986) |
SVM | 82.67 | 80.45 | 80.45 | 0.824 (0.805, 0.844) |
BP | 85.36 | 85.17 | 93.43 | 0.892 (0.849, 0.934) |
CNN | 72.37 | 77.18 | 66.98 | 0.724 (0.670, 0.779) |
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