Improving the Accuracy of Artificial Intelligence Models in Nutrition and Health Research Through High-Quality Data Processing

Abstract

Artificial intelligence (AI) has become a new instrument of change in nutrition and health research and is promising an opportunity to enhance dietary evaluation, disease forecast, and personalized health interception. Nevertheless, the precision and generalizability of the AI models is limited by the quality of input information. Lack of information / Missing values, variation in reporting dietary/dietary recall reporting, non-homogenous health records, and unorganized clinical data are some of the challenges that often create an algorithmic bias and a lower predictive validity. This article explores the importance of high quality of data processing to achieve better accuracy of AI models in nutrition and health sciences. Based on empirical research and methodological approaches, this paper sheds light on the importance of the data cleaning, data integration, feature engineering and validation processes in eliminating errors and enhancing model performance. It also highlights the necessity of specifying standard procedures of data collection and harmonized ontologies to make diverse data interoperable. Privacy, equity, and introduction of bias, among other ethical issues, are cited as critical to the responsible use of AI applications. These results might indicate the potential of adding strong data pipelines to boost technical precision and, concomitantly, clinical and public health applicability, thereby promoting the missions of precision nutrition and evidence-based healthcare. This paper will help advance the discussion within the context of methodological rigor, data governance, and the ethical