A Machine Learning Classification Technique for Predicting Prostate Cancer
| Autor: | P. Rajesh, Hari K. Vege, Mansoor Alam, Mansour Tahernezhadi, B. Mohammed Ismail |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Oncology
medicine.medical_specialty business.industry 02 engineering and technology medicine.disease Logistic regression Smoking history 03 medical and health sciences Prostate cancer Prostate-specific antigen Statistical classification 0302 clinical medicine 030220 oncology & carcinogenesis Internal medicine Tumor stage 0202 electrical engineering electronic engineering information engineering Medicine 020201 artificial intelligence & image processing business Tumor node metastasis |
| Zdroj: | EIT |
| DOI: | 10.1109/eit48999.2020.9208240 |
| Popis: | This paper presents and validates various classification techniques on supervised machine learning (ML) for predicting prostate cancer. A modified Logistic Regression (LR) classifier is proposed and implemented on patients who are susceptible to prostate cancer. The proposed classification technique uses both clinical and tumor stage characteristics. Clinical characteristics considered are BMI, age, cystitis infections, and smoking history. Tumor stage characteristics are stages of Tumor Node Metastasis (TNM), American Joint Committee on Cancer (AJCC) and Prostate Specific Antigen (PCA). Results obtained show improvement in accuracy and positive prediction value (PPV) as compared to existing classifiers. Results are compared and validated with performance measures of Specificity (Sp) and Sensitivity (Se), recording a minimum of 3% improvement in Pc prediction accuracy. The implemented ML classification technique also shows a clinical impact on Pc diagnosis with a 4 % improvement in Sp. |
| Databáze: | OpenAIRE |
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