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.