Predictive Analytics in Personalized Medicine: Leveraging Machine Learning for Patient-Specific Treatments

Abstract

Personalized medicine strives to customize treatments based on individual patient profiles, thereby enhancing healthcare outcomes. This paper introduces a comprehensive machine learning framework that harnesses predictive analytics to create patient-specific treatment plans. Our approach integrates gradient boosting machines (GBM) and recurrent neural networks (RNN), along with Recurrent Generative Adversarial Networks (RNN-GAN), to analyze longitudinal patient data encompassing genetic, clinical, and lifestyle factors. The hybrid models were tested on datasets, including MRI images, genomic sequences, and patient records. The GBM+RNN model demonstrated superior accuracy, achieving 97% for MRI images, 96% for genomic data, and 95% for patient records. The RNN+GAN model also performed exceptionally well, achieving 95% for MRI images, 94% for genomic data, and 93% for patient records. These results highlight the potential of advanced machine learning techniques, such as GBM, RNN, and RNN-GAN, to improve the precision of personalized medicine, paving the way for more effective and tailored healthcare interventions.