Blood Glucose Prediction Using Convolutional Long Short-Term Memory Algorithms

Diabetes Mellitus is one of the preeminent causes of death to date. Effective procedures are necessary to prevent diabetes and avoid complications that may cause early death. A common approach is to control patient blood glucose, which necessitates a periodic measurement of blood glucose concentration. This study developed a blood glucose prediction system using a convolutional long short-term memory (Conv-LSTM) algorithm. Conv-LSTM is a variation of LSTM algorithms that are suitable for use in time series problems. Conv-LSTM overcomes the lack in the LSTM algorithm because the latter algorithm cannot access the content of previous memory cells when its output gate has closed. We tested the algorithm and varied the experiment to check the effect of the cross-validation ratio between 70:30 and 80:20. The study indicates that the cross-validation using a ratio of 70:30 data split is more stable compared to one with 80:20 data split. The best result shows a measure of 21.44 in RMSE and 8.73 in MAE. With the application of conv-LSTM using correct parameters and selected data split, our experiment attains accuracy comparable to the regular LSTM.

Analysis of the Causal Relationship of Body Image Factors in Patients with Cancer

Patients with cancer can potentially experience the negative impacts of treatment. Physical conditions due to illness and therapy can affect the patient's body image. This study aims to find a causal model among body image factors of patients with cancer using the S3C-Latent Method. The measurement of body image of patients with cancer used the BIS questionnaire. One hundred and ninety-nine patients with cancer participated in this study. The results showed the existence of causal relationships between behavior to cognitive factors and duration of illness with reliability scores of 0.8 and 0.6, respectively; from gender to affective factors, illness duration, behavior, and cognitive factors with reliability scores of 0.6, 0.8, 0.65, and 1, respectively. There are also causal relationships from age to affective factors, duration of illness, and cognitive factors with reliability scores of 0.8, 0.7, and 0.9, respectively. The results also showed that affective factors are associated with behavior, cognitive factors, and duration of illness, with reliability scores of 1, 1, and 0.9, respectively. The results showed further the association of cognitive factors and illness duration with a reliability score of 1. We expect that the estimated causal model will serve as a scientific reference for medical experts in developing a better intervention such as treatment.

Location Selection Based on Surrounding Facilities in Google Maps using Sort Filter Skyline Algorithm

Selecting a good location is an essential task in many location-based applications. Intuitively, a place is better than another if there are many good facilities around it. The most popular location selection platform today is Google Maps. Unfortunately, Google Maps has not provided the location selection based on the number of surrounding facilities. Assume a situation when a college student wants to let a house near his campus. Besides the distance from the campus, the student certainly will consider amenities surrounding it, such as food courts, supermarkets, health clinics, and places of worship. The rent house will become a better choice if there are more of these facilities around. Skyline query is a well-known method to select interesting desirable objects. We applied the Sort Filter Skyline (SFS) Algorithm on Google Maps to get a small number of attractive locations based on the number of nearby facilities. This study has succeeded in developing a web-based application that facilitates Google Maps users to search for places based on the figure of surrounding facilities. The time required to do a location search using SFS in Google Maps will increase with the number of surrounding facility types considered by the user.