Design and application of tumor prediction model based on statistical method^.

Area of Science:

• Oncology
• Biostatistics
• Computational Biology

Background:

• Accurate tumor prediction is crucial for early cancer detection and treatment planning.
• Identifying key risk factors enhances the efficacy of predictive models.

Purpose of the Study:

• To develop and compare two distinct tumor prediction models: logistic regression and BP neural network.
• To conduct a sensitivity analysis of risk factors associated with lung cancer.
• To create a practical R language function for implementing these models and analyzing risk factor correlations.

Main Methods:

• Implementation of logistic regression and BP neural network models using the R programming language.
• Application of the models to lung cancer patient data for validation.
• Comparative analysis of model accuracy and feasibility.
• Development of an integrated R function for streamlined tumor prediction and risk factor assessment.

Main Results:

• Both logistic regression and BP neural network models demonstrated accuracy in tumor prediction.
• Sensitivity analysis identified significant risk factors correlating with lung cancer.
• The developed R function efficiently generated prediction models and assessed risk factor impact.

Conclusions:

• The proposed logistic regression and BP neural network models provide reliable tools for tumor prediction.
• The R-based approach offers a practical and efficient method for early cancer detection and risk factor analysis.
• This methodology holds potential for integration into clinical practice for improved cancer diagnostics.