Albert Verasius Dian Sano’s scientific contributions

The Indonesian government continues to deal with nutritional issues on Indonesian children, such as stunting and wasting. Stunting is a common symptom of children at risk of wasting, and they are more likely to develop long-term developmental issues. Malnutrition in children must be identified as soon as possible in order to prevent many instances and provide prompt, effective treatment to keep the condition from getting worse. The goal of this study is to create a model for predicting children's nutritional status using supervised machine learning algorithms. In order to create a nutritional status prediction model for kids under 5 based on physical examination, this study compares three supervised machine learning algorithms. C4.5 Decision Tree, K-Nearest Neighbors (KNN), and Naive Bayes are the machine learning models. Healthcare Sawah Besar Community Health Service provided its 360 patients with 4 attributes’ worth of record information. A model's performance was evaluated using F1-Score and accuracy. The nutritional condition of children under the age of five can be predicted using the C4.5 Decision Tree, K-Nearest Neighbors, and Naive Bayes algorithms. The Sawah Besar Community Health Service can utilize the model to track the nutritional health of children under five years of age, and it can also assist mothers in understanding the nutritional condition of their children. The outcome reveals that the C4.5 Decision Tree performs best, with an accuracy rate of 89.87% and an F1-Score of 91.67%. The C4.5 Decision Tree approach is the best way for forecasting the nutritional status of children under the age of five, according to the experiment data.KeywordsNutritional statusChildren under 5 years of ageMachine learning

Early detection has already reduced pregnancy risk, complications, emergency situations, and also maternal mortality cases. Our study’s goal was to build on the intelligent application for early risk pregnancy prediction based on machine learning. We examined 997 patient data and 114 attributes from the electronic medical records on primary health care cohort data from the ENA System of the Sawah Besar Primary Health Care. Subsequently, eight attributes were chosen based on the Indonesian Ministry of Health, Maternal and Child Health Handbook, and medical doctor-supervised as classifier attributes. Machine learning and Knowledge Discovery from Data (KDD) technique was also applied to build an intelligent prediction in this work. In addition, we investigated the decision tree C4.5, random forest, and naive bayes algorithms for seeing which one was the right match for our application. The accuracy values for decision tree C4.5, random forest, and naive bayes were 98.01, 98.51, and 68.81%, respectively. On most accuracy measures, the random forest algorithm exceeded the decision tree C4.5 and the naive bayes algorithm. As a consequence, we employed random forest to build the web-based application. Additionally, all three algorithms obtained AUCs ranging from 0.95 to 0.99, indicating perfect prediction accuracy. Our study’s contribution was to pave the way for machine learning potential in intelligent applications for early risk pregnancy prediction. In conclusion, we successfully developed an intelligent application for risk pregnancy prediction based on machine learning and revealed potential implications in providing self-checking and early detection of pregnancy risk based on machine learning. KeywordsDecision tree C4.5Random forestNaïve BayesKnowledge discovery from dataRisk of pregnancy