A Predictive Approach For Assessing Medication Responsiveness in Breast Cancer Through Analysis of Gene Data

Breast cancer remains one of the leading causes of cancer-related deaths worldwide, with treatment responses varying wildly among patients. The inability to predict how an individual patient will respond to a given particular medication is an essential area in optimising treatment plans and better outcomes in such deadly diseases. This research aims to address the challenge of predicting medication responsiveness in breast cancer, analysing genomic data by exploiting publicly available datasets from both The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The datasets are comprised of gene expression profiles, somatic mutations, and clinical data such as patient demographics, tumour subtypes, and treatment outcomes. Preprocessing involved cleaning and normalisation of gene expression values, imputation to address missing data, and selection of essential genes that were most relevant for drug response using feature selection techniques. Its dimensionality was reduced using Principal Component Analysis (PCA). Four machine learning models, including artificial neural networks (ANN), Convolutional Neural Networks (CNN), recurrent neural networks with long short-term memory (RNN-LSTM), and autoencoders, are used in this research. All models were trained, validated, and tested against accuracy, precision, recall, F1 score, and AUC-ROC in that the highest performance was achieved at 98.89% accuracy using the CNN model. The findings point towards the fact that deep learning models, especially CNN, open promising avenues for personalised breast cancer treatment. The findings, therefore, hold significant importance for the application of AI in precision medicine, potentially enabling more accurately targeted therapies, minimising side effects, and ultimately improving patient outcomes in oncology.