Artificial Intelligence Models for Early Disease Detection Using Clinical Data

Abstract

In a variety of chronic and acute diseases, earlier detection of disease is a major determinant of better patient outcomes, better treatment costs savings and reduced mortality.The current diagnostic process, mostly manual, manual interpretation of clinical data, is limited by the capacity of the human mind, by inter-observer variability and by the amount of data produced by modern healthcare systems.Artificial Intelligence (AI) that leverages machine learning (ML) and deep learning (DL) capabilities has become a rapidly evolving technology that can help uncover patterns within very large, heterogeneous and high-dimensional clinical datasets, well in advance of what traditional methods could accomplish.In this research paper, a thorough investigation of AI models for early disease detection from medical data, such as laboratory results, electronic health records (EHR), vital signs, and medical image metadata is conducted.We examine the theoretical underpinnings of important classifiers like Logistic Regression, Support Vector Machines, Random Forests, Gradient Boosting methods, Artificial Neural Networks, Convolutional Neural Networks and Long Short-Term Memory networks and compare their performances using the metrics: accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC).A systematic procedure of data acquisition, data preprocessing, feature engineering, model training, model validation and interpretability is suggested and illustrated using simulated experiments.The results show that ensemble and deep learning algorithms (especially XGBoost and CNN) are more accurate at forecasting than traditional statistical models, but they are less interpretable and more demanding in terms of resources.The paper also highlights these issues of data privacy, class imbalance, explainability of AI models, and clinical integration, and proposes directions for future research such as federated learning, explainable AI, multimodal data fusion for early disease detection, among others.