Predictive Modelling of Pathological Complete Response and Relapse-Free Survival in Cancer Patients
Overview
This repository contains the implementation of a machine learning project aimed at enhancing the accuracy of predicting Pathological Complete Response (PCR) and Relapse-Free Survival (RFS) in chemotherapy-treated breast cancer patients. The project analyzes a dataset with clinical and MRI features across 400 patients to provide additional prognosis information for personalized treatment recommendations.
Introduction
This project aims to improve the predictions of PCR and RFS in breast cancer patients using various machine learning techniques. Accurate predictions can guide treatment decisions and provide better prognostic information.
Dataset
The dataset used in this project is publicly available from The American College of Radiology Imaging Network.
Feature Type
Number of Features
Clinical
10
MRI-based
107
Total Records
400
Methods
Data Preprocessing
Handling Missing Values: Rows with missing values denoted by '999' were removed. Median imputation was used for remaining missing values.
Feature Selection: ANOVA was used to assess the importance of each feature. Features with an f-score above 3.5 were selected.
Model Selection
Model Type
Model
Classification
Logistic Regression
AdaBoost Classifier
Support Vector Machine (SVM)
Voting Classifier
Model Type
Model
Regression
Random Forest Regressor
Support Vector Regressor (SVR)
LASSO Regressor
Ridge Regressor
AdaBoost Regressor
Model Evaluation
Evaluation Type
Metric
Classification
Balanced Accuracy
Precision
Regression
Mean Absolute Error (MAE)
Tuning
Grid Search
Results
The two best results from classification and regression task was evaluated.
Classification Results
Model
Balanced Accuracy
Precision
AdaBoost Classifier
73.43%
70.59%
Voting Classifier
70.68%
83.33%
Regression Results
Model
Mean Absolute Error (MAE)
Random Forest Regressor
20.43
LASSO Regressor
20.84
Conclusion
The AdaBoost Classifier was the best model for predicting PCR due to its balanced accuracy and precision. The Random Forest Regressor was the most effective for predicting RFS with the lowest MAE and robust performance.