Patients admitted to intensive care units (ICUs) often have a higher risk of sepsis due to weakened immune systems. Early sepsis diagnosis is crucial for timely treatment, emphasizing the need to improve the predictive capabilities of sepsis prediction models. Although machine learning models have demonstrated success in predicting sepsis onset, there is limited work done on how model assessment is affected by sequential prediction rather than evaluating on one prediction per patient. This thesis assesses the effectiveness of the evaluation procedures employed by such models and explore different prediction conditions to enhance sepsis prediction. Data was collected from the MIMIC-IV data set,and includes variables commonly used in real ICU settings relevant to sepsis diagnosis. Random onset matching is used to select time points for patients with and without sepsis, with the data analyzed using XGBoost. Evaluation metrics are calculated both once per patient, and is compared to sequential measurements for all patients from 40 hours before sepsis up until sepsis onset. Results shows that a model trained on data close to sepsis onset has strong predictive performance up to 25 hours before sepsis onset. In addition,different restrictive conditions on predictions are considered and evaluated. As the test set is limited it is important that the results are validated further, as it could provide insights regarding interpretation in the practical implementation of similar prediction models for support of healthcare professionals through timely interventions.