The study was conducted at the Health Management Center (HMC) of Taipei Medical University Hospital (TMUH). Electronic medical records (EMRs) were obtained and reviewed from the HMC, which receives approximately 60 to 70 visits per month.

The study was approved by the Institutional Review Board (IRB) of TMUH prior to data collection (TMUH TMU-JIRB number N202003088), in accordance with the original and amended Declaration of Helsinki. The IRB waived the need for informed consent because of the retrospective nature of this study.

The databases and the selected predicting variables (Table 1) were derived from previous publication on MetS and CKD [34-36]. Figure 1 shows an overview of the system and the main functions. Briefly, using a series of complicated procedures, the two databases (MetS and CKD) were connected to an internet platform to construct one integrated system. This web-based system was embedded with ML models to provide various medical evaluations and analyses. The online system was constructed on a server as a web-based environment. The frontend implementation included the programming language JavaScript (Oracle Corp), the framework VueJS (Vue), and the styling Syntactically Awesome Style Sheets (Sass). The backend implementation used Java and R as the programming languages, and all ML calculations and evaluations were conducted using the statistical program R (version 3.6.1, R Foundation for Statistical Computing). The back web framework was Spring Boot (Pivotal Software), connecting the MySQL (Oracle Corp) database as the storage system.

Figure 2 [37-39] shows an overview of the main study population and the validation populations. Briefly, the starting study population included 48,628 EMRs of Taiwanese adults aged over 18 years who underwent a self-paid health examination at TMUH from July 2015 to December 2019. All the study participants completed a self-questionnaire on demographics, existing medical conditions, and the use of medications.

Subsequently, the starting population data underwent data cleaning and preprocessing to form two distinct databases (MetS and CKD) for ML. For the MetS database, there were a total of 1129 participants after the exclusion of participants without FibroScan (Echosens) measurements. For the CKD database, there were a total of 2287 participants after the exclusion of participants without values for creatinine, blood urea nitrogen, and uric acid.

Due to the inconsistent definition of MetS across the world, the ML performance of the MetS database and the CKD database were validated using different study populations. The ML performance of the CKD database was validated using Taiwanese, Italian, US, and Japanese data sets, but the ML performance of the CKD database was only validated using a Taiwanese data set [37-39]. Since different variables may be unavailable in different validation data sets, unavailable variables were simply excluded in ML performance analysis for a balanced comparison.

The ML techniques used in this system included supervised learning models, such as classification and regression tree (CART) and random forest [35,36]. Supervised learning was applied to classify the patients in the training set and predict patients with a specific chronic disease or syndrome in the validation set before the prediction model was available on this system [40,41]. In addition, unsupervised learning (hierarchical clustering using the Ward method and Euclidean distance) was embedded in a heat map, providing classified visualization between new input records and the database. An interactive heat map that could be rearranged or zoomed in and out was applied to this system [42-47].

All outcomes were presented on the web platform after the ML system evaluated the users’ EMRs. Although the ML system was developed on a web-based interface, it could be embedded in the Internet of Medical Things (IoMT) environment, for example, as apps or real-time monitoring systems between several medical centers and hospitals [48-50].

To measure the usability of websites, we invited potential users of the ML system (physicians, medical staff, and potential users) to fill out a system usability scale (SUS) evaluation questionnaire. SUS was chosen as the usability test tool because previous studies found it to be reliable and quick to answer, and the final score is provided with interpretation based on a well-established reference standard [51,52]. In general, the higher the SUS score, the better the usability of the website. Details about the questionnaire design (the 10 questions), score summary, and results of reliability and validity tests are given in Multimedia Appendix 1.

This ML medical system for three common diseases in family medicine (MetS, CKD, and liver diseases) was constructed from EMR subjects who underwent self-paid health examination. Several ML prediction models are applied to the databases, and the outcomes are summarized and presented visually on the website for users and medical staff. The accuracy of predicting MetS reached 90.9%, and AUC was 0.904 in this system. For chronic kidney disease, the prediction accuracy reached 94.7%, and the AUC was 0.982. In general, users who were invited to test this system rated it with good usability and could easily assess their health online through this web-based ML monitoring system.

Decision trees are an important type of ML algorithm for predictive modeling. They are commonly used in data mining with the objective of creating a model that predicts the dependent variable (the target) based on numerous independent variables [34,37].

A decision tree is a nonparametric ML modeling technique used for regression and classification problems. In classification problems, the target variable is categorical, and the tree is used to identify which group or class a target variable would likely fall into. In regression problems, the target variable is continuous, and the tree is used to predict its value. To find solutions, a decision tree makes a sequential, hierarchical decision about the outcome’s variable according to the predictor [55-57].

Hence, CART can provide a visual tree-based diagram for medical practitioners to disseminate health care information to patients. It also helps users to understand the significance of different risk factors for specific diseases. For example, the cut-off controlled attenuation parameter (CAP) score was used to separate patients with MetS and those with other health observations. The CAP score was brought to the attention of users, thereby increasing their awareness of self-health [34].

Random forest, also called random decision forests, is a popular ensemble learning method in ML. Ensemble methods use multiple learning algorithms to improve ML results by combining several decision tree models. This approach allows better predictive performance compared with a single model. Random forest is a parallel ensemble method in which the base learners are generated in parallel. The basic motivation of parallel methods is to exploit independence between the base learners because the error can be reduced dramatically by averaging [58,59]. As random forest provides a bagging technique for feature estimates, it also offers efficient estimates of the test error without incurring the cost of repeated model training associated with cross-validation. Moreover, random forest ranks risk factors in prediction models, which clinicians can use as a reference for diagnosis, and remote users can use to review their risk assessment of related diseases [34,60-62]. For instance, clinicians can refer to significant factors of certain diseases to determine whether those factors exceed the thresholds or not, allowing patients to be more vigilant about their risk of developing such diseases. In addition, sequential ensemble methods such as AdaBoost and XGBoost will be implemented and uploaded to our system in the future.

Hierarchical clustering is a widely used unsupervised learning technique that groups data with similar characteristics. Both agglomerative and divisive approaches use dendrograms for the results. A heat map is a color graphical representation of data, which uses a matrix with color gradients to present the similarity of data.

Many studies on genetic bioinformatics and bacterial ecology have used heat maps for the analysis of large and complicated data sets, and some medical studies have used heat maps with clustering to present the relationship between various biomarkers according to their characteristics [34,63-65]. Furthermore, our system provides an interactive clustering heat map for health care. From the perspective of big data, users can evaluate their health status by using ML models and EMR databases. In addition to online health evaluation, in the future, this system could be implemented into different IoMT to assist medical practitioners in achieving real-time health evaluations and monitoring remote patients or patients in specific wards. For the heat map, the EMR data of users were grouped into clusters of patients with diseases in the database; they would then be classified as clinically high-risk objects requiring close attention in the clinical setting [34]. Therefore, whether it is applied in preventive medicine for health management, in a monitoring system for critical care, or in the telemedicine environment, our system can provide real-time monitoring and help predict patient conditions.

To the best of our knowledge, this is the first web-based machine learning system based on self-paid health examination subjects that can provide an online self-health evaluation for several common diseases (MetS, CKD, and liver diseases). The version 1.0 web-based system still has several limitations that may be improved in the next update. First, the 1.0 system is not yet ready for embedding into a hospital for real-time assessment. We are currently working on an improved system to accept unstructured data input and multimodal data, which are especially essential for the prediction of eye diseases such as macular degeneration. Second, the 1.0 system did not have a user login or account security function. Retrievable prediction and security will be improved as the system is matured for hospital embedment. Third, the 1.0 system does not have whole dynamic analyses such as an interactive decision tree; whole dynamic analyses will be incorporated in subsequent versions to improve communication between the medical staff and patients.

In the future, more clustering algorithms will be implemented in subsequent versions to make the prediction results more robust and reliable. Although the 1.0 system can currently only evaluate three chronic diseases (MetS, CKD, and liver diseases) frequently encountered in family medicine, more chronic disease prediction models, such as those for coronary artery disease, will be added in the near future.

We constructed an ML health monitoring system to offer an online health assessment service to medical units, telemedicine patients, and all health-conscious users worldwide. Our aim is that this system will be implemented in medical centers as a real-time patient monitoring system and provide regular health evaluations for telemedicine patients. Online users can now access our platform and use ML technology to estimate their health status, increasing self-health awareness.