Figueiredo, DianaDuarte, A. P.Cunha, Carlos2024-10-082024-10-082024Figueiredo, D.M., Duarte, R.P., Cunha, C.A. (2024). Predictive Model for Estimating Body Weight Based on Artificial Intelligence: An Integrated Approach to Pre-processing and Evaluation. In: de la Iglesia, D.H., de Paz Santana, J.F., López Rivero, A.J. (eds) New Trends in Disruptive Technologies, Tech Ethics, and Artificial Intelligence. DiTTEt 2024. Advances in Intelligent Systems and Computing, vol 1459. Springer, Cham. https://doi.org/10.1007/978-3-031-66635-3_3978303166634697830316663532194-53572194-5365http://hdl.handle.net/10400.19/8580Body weight is much more than just a number on a scale. This value can indicate various diseases, as both excess and insufficient weight have implications for an individual’s health. Excess weight is associated with heart disease, obesity, diabetes, high blood pressure, and respiratory disorders, among others. Meanwhile, extreme underweight is associated with problems such as nutritional deficiency, weakened immune system, osteoporosis, and hormonal imbalances. Due to these issues, there is a need to monitor and analyse body changes to adopt a diet and lifestyle balanced with individual needs. The weight control process is complicated and depends on various factors. This paper aims to develop a machine-learning model to predict future weight based on dietary records, physical exercise, and basal metabolic rate to demonstrate three days’ impact on future weight. Results of the model’s performance show that the coefficient of determination yielded a value of 0.75, which is considered good for this metric. The mean square and absolute errors demonstrate that the model could learn patterns in the data without significant overfitting.engArtificial Intelligencebook part2024-09-21cv-prod-415056610.1007/978-3-031-66635-3_3