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Prostate cancer is a prevalent disease among men over 55 years old, with projections indicating 70,000 new cases annually in India by 2040 according to The Lancet. Early diagnosis is crucial. This research aims to enhance the diagnostic accuracy of prostate cancer classification models by combining feature extraction with a Feed forward Artificial Neural Network (ANN). The dataset, sourced from the National Cancer Institute under project ID PLCO-934, consists of screening data from 177,314 patients with 80 features. Classification with the Feed forward ANN showed an improved accuracy of 94.56%. Further application of the feature selection method in analysis identified five principal components, and an impressive accuracy of 98.2% with the Feed forward ANN classifier. These findings underscore the importance of feature selection following feature extraction and the effectiveness of the Feed forward ANN model for prostate cancer classification.

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Advanced Feature Selection Techniques Using PCA-RFECV for Accurate Data Driven Prostate Cancer Diagnosis

  • 会议论文

  • 发表日期：

    2026年

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  • Sonam Lata,
  • Priya Dubey,
  • Fadwa Alrowais,
  • Basel Bilal,
  • Shabana Urooj

摘要

Prostate cancer is a prevalent disease among men over 55 years old, with projections indicating 70,000 new cases annually in India by 2040 according to The Lancet. Early diagnosis is crucial. This research aims to enhance the diagnostic accuracy of prostate cancer classification models by combining feature extraction with a Feed forward Artificial Neural Network (ANN). The dataset, sourced from the National Cancer Institute under project ID PLCO-934, consists of screening data from 177,314 patients with 80 features. Classification with the Feed forward ANN showed an improved accuracy of 94.56%. Further application of the feature selection method in analysis identified five principal components, and an impressive accuracy of 98.2% with the Feed forward ANN classifier. These findings underscore the importance of feature selection following feature extraction and the effectiveness of the Feed forward ANN model for prostate cancer classification.