The measurement of Blood Urea Nitrogen (BUN) is an important test for examining the kidney condition. BUN is a waste product that is eliminated from the body by the kidneys. Increased BUN level often indicates renal diseases. The invasive blood test is used as the standard BUN testing method. In this paper, we have demonstrated an effective and innovative method for noninvasive BUN prediction from Ten-second fingertip video. We use the Ten-second fingertip video of human subjects to create photo plethysmography (PPG) features through signal processing and feature engineering techniques. We utilize Generative Adversarial Networks (GAN), a type of Generative AI, as a data augmentation method to create synthetic data based on our original data. Then, we use the PPG features and Gold standard BUN level of subj ects to develop four regression models before and after the implementation of GAN. We compare all models' performance in terms of MAE, MSE, and RMSE. Before applying GAN, ANN outperforms other models with an MAE of 1.17, MSE of 2.62, and RMSE of 1.62. We find that GAN implementation significantly improves every model's performance and provides lower MAE, MSE, and RMSE. After applying GAN, ANN performs best among all other models and shows the lowest MAE of 0.69, MSE of 1.03, and RMSE of 1.01. With these outstanding results, our noninvasive method of BUN prediction has the potential to offer enhanced patient comfort, early detection, continuous and remote monitoring of renal health.

In this paper, we introduce a machine learning (ML)-based approach for Chronic Kidney Disease (CKD) and diabetes prediction and perform feature effect analysis through SHAP (SHapley Additive exPlanations). We utilize two publicly available clinical datasets and build five ML classifier models for the analysis. Among all models, CatBoost provides the best performance for both CKD and diabetes prediction. Our CatBoost model has an accuracy of 0.95, mean 10-fold cross-validation of 0.96, ROC-AUC score of 0.99, precision of 0.96, recall of 0.96, and F1-score of 0.96 for CKD. The CatBoost model shows an accuracy of 0.99, mean 10-fold cross-validation of 0.97, ROC-AUC score of 1.0, precision of 1.0, recall of 0.98, and F1-score of 0.99 for diabetes. We perform comprehensive feature effect analysis by computing SHAP values. This gives insights into the contribution of every feature to the model's predictions. The achieved results highlight that CatBoost is a robust choice for accurate and reliable predictions of CKD and diabetes. The findings of this study contribute to the corresponding field of ML approaches for medical diagnosis and illustrate the significance of feature effect analysis in understanding model predictions. With excellent results, our research has the potential to enhance the clinical decision-making process and improve patient outcomes.

Diabetic eye diseases, particularly Diabetic Retinopathy, is the leading cause of vision loss worldwide and can be prevented by early diagnosis through annual eye-screenings. However, cost, health care disparities, cultural limitations, etc. are the main barriers against regular screening. Eye-screenings conducted in community events with native-speaking staffs can facilitate regular check-up and development of awareness among underprivileged communities compared to traditional clinical settings. However, there are not sufficient technology support for carrying out the screenings in community settings with collaboration from community partners using native languages. In this paper, we have proposed and discussed the development of our software framework, “Mobile Teleophthalmology in Community Settings (mTOCS)”, that connects the community partners with eye-specialists and the Health Department staffs of respective cities to expedite this screening process. Moreover, we have presented the analysis from our study on the acceptance of community-based screening methods among the community participants as well as on the effectiveness of mTOCS among the community partners. The results have evinced that mTOCS has been capable of providing an improved rate of eye-screenings and better health outcomes.

This paper describes a cloud-based heart rate (HR) and heart rate variability (HRV) monitor, which can monitor an individual's heart rate and heart rate variability using minimal resources at the user's end. This HR and HRV monitor does not involve any contact sensors or costly medical equipment, and it does not require a high-definition camera to video record the user's face. This HR and HRV monitoring system simply requires the webcam of a personal computer or the camera of a mobile device such as a cell phone or tablet in order to video record the face of the user. The system utilizes the resources of the user's device while recording and encoding the video segments before transferring them to a server. All the processes to calculate the HR and HRV occur at the server.

This paper describes a mobile-based care system (mCARE), which is a novel tool to routinely and systematically collect behavior and developmental parameter children with Autism Spectrum Disorder (ASD) from the low-and middle-income countries (LMICs) like Bangladesh. It is the first clinical decision support system for the ASD children in LMICs that has been proved efficient to assist them in data-driven decision making by using a remote Experience Sampling Method (ESM). Unlike other ESM, instead of taking a report from patients, mCARE introduce caregivers as the reporter. mCARE has deployed Value Sensitive Design (VSD) and Human-Computer Interaction (HCI) in its design process as well as Fogg's Behavior Model (FBM) to assist caregivers in developing and sustain the habit of logging data regularly over a long period. mCARE has accumulated its goal by implementing three tools: mCARE-APP, mCARE-SMS, and mCARE-DMP. The successful completion of the project using this mCARE can ensure the overall quality of care (QoC) of ASD in LMICs, including Bangladesh. The usability and cost-benefit analysis have also shown the potential of mCARE to be scaled to serve a large population in Bangladesh and beyond.