Predicting malnutrition-based anemia in geriatric patients using machine learning methods.

Background: Anemia due to malnutrition may develop as a result of iron, folate and vitamin B12 deficiencies. This situation poses a higher risk of morbidity and mortality in the geriatric population than in other age groups. Therefore, early diagnosis of anemia and early initiation of treatment is very important. This study aims to predict the diagnosis of anemia with using machine learning (ML) methods in geriatric patients followed in an outpatient clinic.

Methods: In line with the purpose of the study, anemia classification was made by analysing patients' hemogram and biochemistry blood values and medical data such as malnutrition, physical and cognitive activity scores with ML methods.

Results: In our data set consisting of 438 patient observations, the most successful ML algorithm was the J48 algorithm with 97.77% accuracy. In the continuation of the study, the predictive performance of anemia was investigated by excluding blood values and selecting only attributes consisting of malnutrition and physical activity scores. In this case, the most successful prediction was obtained with the Random Forest algorithm with 85.39% accuracy.

Conclusions: The study showed that anemia can be predicted with high accuracy in geriatric patients without hemogram data. Additionally, our geriatric data set was shared with researchers for future research. Thus, it has contributed to the literature by opening a new path for studies on subjects such as comparing classification performances with new methodologies or predicting different diseases in geriatric patients.