Machine Learning for Predicting Patient Outcomes: a Study on Treatment Efficacy

EasyChair Preprint 15085

Abstract

Advancements in machine learning (ML) are transforming the healthcare industry by enabling the prediction of patient outcomes based on complex, multi-dimensional data. This study explores the use of ML models to predict treatment efficacy across various medical conditions, focusing on improving patient outcomes and personalizing treatment plans. Traditional methods for predicting outcomes, such as clinical judgment and statistical models, often fall short in handling vast amounts of patient data and variability in treatment response. In contrast, ML algorithms, including decision trees, support vector machines, and neural networks, offer the potential for more accurate and data-driven predictions.

The study collected patient data from electronic health records (EHRs) and clinical trials, focusing on demographic information, clinical features, and treatment types. Preprocessing techniques, including data cleaning and feature selection, were applied to ensure high-quality input for the models. A range of ML algorithms was then trained, evaluated using cross-validation, and compared based on performance metrics such as accuracy, precision, and recall. Key features influencing treatment outcomes were identified, and model interpretability tools like SHAP values were used to explain predictions.

Keyphrases: Electronic Health Records, machine learning, statistical models

@booklet{EasyChair:15085,
  author    = {Ayuns Luz and David Ray},
  title     = {Machine Learning for Predicting Patient Outcomes: a Study on Treatment Efficacy},
  howpublished = {EasyChair Preprint 15085},
  year      = {EasyChair, 2024}}