Predicting Medicine Adherence with Precision: An MLP-Based Approach for Personalized Healthcare

In the realm of personalized healthcare, ensuring patient adherence to prescribed pharmacological regimens is identified as a crucial factor in achieving positive health outcomes. This research study presents a special methodology that utilizes Multilayer Perceptron (MLP) neural networks to properly predict patient medication adherence. The methodology places a specific focus on achieving high levels of precision. The data preprocessing methods involved removing duplicate columns, identifying and eliminating outliers, and normalizing the dataset by z-score normalization. The preliminary results of this inquiry exhibit a significant achievement, as indicated by a maximum validation accuracy of 0.894%. The shown accuracy in predicting medication adherence underscores the promise of the multilayer perceptron (MLP)-based approach in tailoring healthcare treatments to address the specific needs of individual patients optimizing medication adherence forecasts, healthcare practitioners can improve their allocation of resources and interventions, resulting in better patient outcomes and lower healthcare costs. Further exploration and refinement of this approach hold promise for enhancing the effectiveness of personalized treatment and contributing to the well-being of patients on a global scale.