Neural Network Implementation for CRC Awareness Prediction

Abstract

Artificial Intelligence (AI) is prevalent, driven by the resurgence of machine learning (ML) and advancements in data-driven applications, which have created new opportunities to surpass traditional statistical limits in health analytics. This study develops and assesses a deep artificial neural network (ANN) to predict public awareness of Colorectal Cancer (CRC) using six demographic and socioeconomic factors: age, gender, marital status, educational level, work status, and place of residence. Primary data were collected through a survey administered to a sample of 1,229 Lebanese individuals.

We systematically describe the data preprocessing steps, including handling missing values, encoding categorical variables, and scaling features. We specify the model architecture, which includes input, output, and one or more hidden layers, along with training optimization methods such as class weighting, early stopping, and learning rate scheduling. The model is evaluated using thorough cross-validation and held-out testing, with metrics like discrimination and calibration assessments, including accuracy percentages and confidence scores. An explainability technique, including feature importance analysis, is employed to enhance transparency. The proposed network demonstrates reliable binary classification performance while emphasizing interpretability, deployment readiness, and the reproducible integration of machine learning into healthcare analytics.