Search

Purpose: This study aims to find out the best model of the classifier for diabetes prediction in the United States. Methodology: In this research paper, we apply three classification algorithms, namely, Naïve Bayes, Decision Tree and K-Nearest Neighbour to work on the healthcare problem, of early detection of diabetes patients. The data are extracted from Kaggle and pre-processed to ensure the data is cleaned. The dataset is analysed using exploratory data analysis (EDA) and study the correlation between the parameters. The parameters which is not suitable are dropped out for better feature selection and modelling. The model is tested with different parameters, that are, accuracy score, classification report, MSE and RMSE. After the models built, the models are hyperparameter tunned to increase the accuracy of each model. Grid search is one of the functions used to search for the best optimizer for the model. The data are tabulated, and the results are compared for further discussion. Results: The study findings show that the decision tree model has the best result after the hyperparameter tunning. The results are highest test accuracy score (74.24%), with the lowest MSE (0.2576) and RMSE value (0.5075) and strong precision as well as recall for the prediction of no diabetes and diabetes. The evaluation rate shows that the model is the best algorithm for the prediction of diabetes for Americans. [ABSTRACT FROM AUTHOR]

This literature review aims to provide and analyze an overview of STEAM research from 2011 to October 2021. The Springer database identifies the journal articles. This study used a Systematic Literature Review (SLR) [1] with a prismatic flow diagram that can show inclusion activities and data search results [2]. There are 470 articles spread across 12 sub-disciplines, and after each paper from all the documents has been double-checked, 124 articles have met the inclusion criteria and were selected for review. The result obtained from the STEAM research articles has progressed from time to time. Of 72 articles analyzed, the most productive journal name is the International Journal of STEM Education, with the United States of America as the first rank of publishing STEAM research articles. The qualitative research method is the most widely used in STEAM research, with the most research instrument using semi-structured interviews. The effect of research articles has a 99% positive impact on the research. It can include more aspects and articles to analyze and should be able to take advantage of a longer period in conducting literature studies in the future. [ABSTRACT FROM AUTHOR]