Early detection of Type 2 diabetes is necessary for its prevention. The prediction models for detection systems normally employ common factors that may not properly fit all persons having different health conditions. Therefore, this study proposes a method for type 2 diabetes prediction with factors representing personal health conditions. More specifically, this study proposes a novel prediction method named Average Weighted Objective Distance (AWOD) based on the assumption that the individual has diverse health conditions resulting from different individual factors, a requirement for an effective prediction model. AWOD is a modification of Weighted Objective Distance (WOD) by applying information gain to reveal significant and insignificant individual factors having different priorities, which are represented by different weights. For AWOD, the data set is divided into a training set used to determine all relevant thresholds and constant values required for AWOD calculation and the testing set. In particular, AWOD is designed for binary classification problems with a relatively small dataset. To validate the proposed method, two datasets from open sources, Pima Indians Diabetes (Dataset 1) and Mendeley Data for Diabetes (Dataset 2) each containing 392 records, were studied. The prediction performance for both datasets is compared with the machine learning-based prediction methods, including K-Nearest Neighbors, Support Vector Machines, Random Forest, and Deep Learning. The comparison results showed that the proposed method provided 93.22% and 98.95% accuracy for Dataset 1 and Dataset 2, respectively, which are higher than those provided by other machine learning-based methods.

Cardiovascular diseases, specifically heart failure and aortic stenosis, are considered common and deadly, with the additional risk of developing dementia in the elderly population. Early diagnosis can help prevent or alleviate these diseases and potentially reduce mortality rates. Machine learning algorithms, especially gradient boosting (GB), can effectively predict the presence of these diseases through binary classification using demographic and medical data. However, research has yet to combine data from all three diseases for multiclass classification, which is the purpose of the present study. Using a dataset collected from Chiang Rai Prachanukroh Hospital, Chiang Rai, Thailand, a GB-based model is proposed for the multiclass classification of elderly people with heart failure, aortic stenosis, and dementia, with the inclusion of feature engineering techniques for maximum accuracy. Other existing methods, including decision tree, support vector machine, k-nearest neighbors, random forest, and extra trees were applied for comparison. The Optuna framework was used with the tree-structured Parzen estimator for hyperparameter optimization. The results produced by each classifier were compared using various performance metrics, namely precision, recall, F1 score, accuracy, the area under the receiver operating characteristic curve, the area under the precision-recall curve, and the Matthews correlation coefficient. The results are presented separately for each machine learning algorithm for comparison. Based on these metrics, it can be concluded that our proposed GB-based model outperformed other comparative models after applying feature engineering techniques.