A comprehensive screening method using machine learning and many factors (biological characteristics, Helicobacter pylori infection status, endoscopic findings and blood test results), accumulated daily as data in hospitals, could improve the accuracy of screening to classify patients at high or low risk of developing gastric cancer. We used XGBoost, a classification method known for achieving numerous winning solutions in data analysis competitions, to capture nonlinear relations among many input variables and outcomes using the boosting approach to machine learning. Longitudinal and comprehensive medical check-up data were collected from 25,942 participants who underwent multiple endoscopies from 2006 to 2017 at a single facility in Japan. The participants were classified into a case group (y = 1) or a control group (y = 0) if gastric cancer was or was not detected, respectively, during a 122-month period. Among 1,431 total participants (89 cases and 1,342 controls), 1,144 (80%) were randomly selected for use in training 10 classification models; the remaining 287 (20%) were used to evaluate the models. The results showed that XGBoost outperformed logistic regression and showed the highest area under the curve value (0.899). Accumulating more data in the facility and performing further analyses including other input variables may help expand the clinical utility.

BackgroundA 75-g oral glucose tolerance test (OGTT) provides important information about glucose metabolism, although the test is expensive and invasive. Complete OGTT information, such as 1-hour and 2-hour postloading plasma glucose and immunoreactive insulin levels, may be useful for predicting the future risk of diabetes or glucose metabolism disorders (GMD), which includes both diabetes and prediabetes.ObjectiveWe trained several classification models for predicting the risk of developing diabetes or GMD using data from thousands of OGTTs and a machine learning technique (XGBoost). The receiver operating characteristic (ROC) curves and their area under the curve (AUC) values for the trained classification models are reported, along with the sensitivity and specificity determined by the cutoff values of the Youden index. We compared the performance of the machine learning techniques with logistic regressions (LR), which are traditionally used in medical research studies.MethodsData were collected from subjects who underwent multiple OGTTs during comprehensive check-up medical examinations conducted at a single facility in Tokyo, Japan, from May 2006 to April 2017. For each examination, a subject was diagnosed with diabetes or prediabetes according to the American Diabetes Association guidelines. Given the data, 2 studies were conducted: predicting the risk of developing diabetes (study 1) or GMD (study 2). For each study, to apply supervised machine learning methods, the required label data was prepared. If a subject was diagnosed with diabetes or GMD at least once during the period, then that subject’s data obtained in previous trials were classified into the risk group (y=1). After data processing, 13,581 and 6760 OGTTs were analyzed for study 1 and study 2, respectively. For each study, a randomly chosen subset representing 80% of the data was used for training 9 classification models and the remaining 20% was used for evaluating the models. Three classification models, A to C, used XGBoost with various input variables, some including OGTT data. The other 6 classification models, D to I, used LR for comparison.ResultsFor study 1, the AUC values ranged from 0.78 to 0.93. For study 2, the AUC values ranged from 0.63 to 0.78. The machine learning approach using XGBoost showed better performance compared with traditional LR methods. The AUC values increased when the full OGTT variables were included. In our analysis using a particular setting of input variables, XGBoost showed that the OGTT variables were more important than fasting plasma glucose or glycated hemoglobin.ConclusionsA machine learning approach, XGBoost, showed better prediction accuracy compared with LR, suggesting that advanced machine learning methods are useful for detecting the early signs of diabetes or GMD. The prediction accuracy increased when all OGTT variables were added. This indicates that complete OGTT information is important for predicting the future risk of diabetes and GMD accurately.

This paper addresses the problem of filtering with a state-space model. Standard approaches for filtering assume that a probabilistic model for observations (i.e. the observation model) is given explicitly or at least parametrically. We consider a setting where this assumption is not satisfied; we assume that the knowledge of the observation model is only provided by examples of state-observation pairs. This setting is important and appears when state variables are defined as quantities that are very different from the observations. We propose Kernel Monte Carlo Filter, a novel filtering method that is focused on this setting. Our approach is based on the framework of kernel mean embeddings, which enables nonparametric posterior inference using the state-observation examples. The proposed method represents state distributions as weighted samples, propagates these samples by sampling, estimates the state posteriors by Kernel Bayes' Rule, and resamples by Kernel Herding. In particular, the sampling and resampling procedures are novel in being expressed using kernel mean embeddings, so we theoretically analyze their behaviors. We reveal the following properties, which are similar to those of corresponding procedures in particle methods: (1) the performance of sampling can degrade if the effective sample size of a weighted sample is small; (2) resampling improves the sampling performance by increasing the effective sample size. We first demonstrate these theoretical findings by synthetic experiments. Then we show the effectiveness of the proposed filter by artificial and real data experiments, which include vision-based mobile robot localization.

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