Analysis for Predictive Analytics for Chemotherapy Effectiveness Using Machine Learning

Executive Summary: Predictive Analytics for Chemotherapy Effectiveness Using Machine Learning

Chemotherapy is an important cancer treatment, but its effectiveness varies significantly among patients due to factors like tumor type, genetics, and overall health. Accurately predicting treatment response is crucial for optimizing treatment plans, reducing side effects, and improving patient outcomes. This study demonstrates how machine learning (ML) models, including Support Vector Machines (SVM), Random Forests, and Neural Networks, can accurately predict patient response to chemotherapy. By analyzing patient medical history, genomic data, treatment protocols, and tumor characteristics, these models identify patients most likely to respond or not respond. Clinical and patient records provide diverse data for training and testing across various cancer types. The study employs both supervised and unsupervised learning to handle complex, high-dimensional data and identify key drivers of treatment success. Performance metrics like accuracy, precision, recall, and AUC confirm the models' efficacy. The results indicate that ML-driven predictive analytics can personalize treatment plans, leading to more efficient, effective cancer procedures, reduced side effects, and better patient outcomes.