Use of a Deep Learning Approach for the Sensitive Prediction of Hepatitis B Surface Antigen Levels in Inactive Carrier Patients

Deep learning is a subset of machine learning that can be employed to accurately predict biological transitions. Eliminating hepatitis B surface antigens (HBsAgs) is the final therapeutic endpoint for chronic hepatitis B. Reliable predictors of the disappearance or reduction in HBsAg levels have not been established. Accurate predictions are vital to successful treatment, and corresponding efforts are ongoing worldwide. Therefore, this study aimed to identify an optimal deep learning model to predict the changes in HBsAg levels in daily clinical practice for inactive carrier patients. We identified patients whose HBsAg levels were evaluated over 10 years. The results of routine liver biochemical function tests, including serum HBsAg levels for 1, 2, 5, and 10 years, and biometric information were obtained. Data of 90 patients were included for adaptive training. The predictive models were built based on algorithms set up by SONY Neural Network Console, and their accuracy was compared using statistical analysis. Multiple regression analysis revealed a mean absolute percentage error of 58%, and deep learning revealed a mean absolute percentage error of 15%; thus, deep learning is an accurate predictive discriminant tool. This study demonstrated the potential of deep learning algorithms to predict clinical outcomes.

A Multivariate Multi-Step LSTM Forecasting Model For Tuberculosis Incidence With Model Explanation In Liaoning Province, China

Background: In addition to COVID-19, tuberculosis (TB) is the respiratory infectious disease with the highest incidence in China. We aim to design a series of forecasting models and find the factors that affect the incidence of TB, thereby improving the accuracy of the incidence prediction. Results: In this paper, we developed a new interpretable prediction system based on the multivariate multi-step Long Short-Term Memory (LSTM) model and SHapley Additive exPlanation (SHAP) method. Moreover, four accuracy measures are introduced into the system: Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, and symmetric Mean Absolute Percentage Error. Meanwhile, the Autoregressive Integrated Moving Average (ARIMA) model and seasonal ARIMA model are established. The multi-step ARIMA-LSTM model is proposed for the first time to examine the performance of each model in the short, medium, and long term, respectively. Compared with the ARIMA model, each error of the multivariate 2-step LSTM model is reduced by 12.92%, 15.94%, 15.97%, and 14.81% in the short term. The 3-step ARIMA-LSTM model achieved excellent performance, with each error decreased to 15.19%, 33.14%, 36.79%, and 29.76% in the medium and long term. We provide the local and global explanation of the multivariate single-step LSTM model in the field of incidence prediction, pioneering. Conclusions: The multivariate 2-step LSTM model is suitable for short-term forecasts, and the 3-step ARIMA-LSTM model is appropriate for medium and long-term forecasts. In addition, the prediction effect was better than similar TB incidence forecasting models. The SHAP results indicate that the five most crucial features are maximum temperature, average relative humidity, local financial budget, monthly sunshine percentage, and sunshine hours.

Heart Rate and Distance Measurement of Two Multisport Activity Trackers and a Cellphone App in Different Sports: A Cross-Sectional Validation and Comparison Field Study

Options for monitoring sports have been continuously developed by using activity trackers to determine almost all vital and movement parameters. The aim of this study was to validate heart rate and distance measurements of two activity trackers (Polar Ignite; Garmin Forerunner 945) and a cellphone app (Polar Beat app using iPhone 7 as a hardware platform) in a cross-sectional field study. Thirty-six moderate endurance-trained adults (20 males/16 females) completed a test battery consisting of walking and running 3 km, a 1.6 km interval run (standard 400 m outdoor stadium), 3 km forest run (outdoor), 500/1000 m swim and 4.3/31.5 km cycling tests. Heart rate was recorded via a Polar H10 chest strap and distance was controlled via a map, 400 m stadium or 50 m pool. For all tests except swimming, strong correlation values of r > 0.90 were calculated with moderate exercise intensity and a mean absolute percentage error of 2.85%. During the interval run, several significant deviations (p < 0.049) were observed. The swim disciplines showed significant differences (p < 0.001), with the 500 m test having a mean absolute percentage error of 8.61%, and the 1000 m test of 55.32%. In most tests, significant deviations (p < 0.001) were calculated for distance measurement. However, a maximum mean absolute percentage error of 4.74% and small mean absolute error based on the total route lengths were calculated. This study showed that the accuracy of heart rate measurements could be rated as good, except for rapid changing heart rate during interval training and swimming. Distance measurement differences were rated as non-relevant in practice for use in sports.

Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak

Background Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models including various machine learning (ML) models are being used by the research community to track and predict the trend of the epidemic, and also in developing appropriate strategies to combat and manage its spread. Methods In this paper, we present a comparative analysis of various ML approaches including Support Vector Machine, Random Forest, K-Nearest Neighbor and Artificial Neural Network in predicting the COVID-19 outbreak in the epidemiological domain. We first apply the autoregressive distributed lag (ARDL) method to identify and model the short and long-run relationships of the time-series COVID-19 datasets. That is, we determine the lags between a response variable and its respective explanatory time series variables as independent variables. Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic. Results Statistical measures—Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE)—are used for model accuracy. The values of MAPE for the best-selected models for confirmed, recovered and deaths cases are 0.003, 0.006 and 0.115, respectively, which falls under the category of highly accurate forecasts. In addition, we computed 15 days ahead forecast for the daily deaths, recovered, and confirm patients and the cases fluctuated across time in all aspects. Besides, the results reveal the advantages of ML algorithms for supporting the decision-making of evolving short-term policies.

Peramalan Harga Emas Menggunakan Metode Average Based And Fuzzy Logic Relationship

Emas merupakan alternatif yang cenderung dipilih kebanyakan orang untuk berinvestasi karena beberapa alasan, salah satunya menguntungkan. Untuk memperoleh keuntungan yang optimal, pelaku investasi harus mengetahui pergerakan harga emas sehingga pelaku investasi tahu kapan harus membeli emas dan kapan harus menjual emas. Pergerakan harga emas dapat dipantau dengan peramalan. Metode peramalan yang digunakan dalam penelitian ini adalah average based and fuzzy logic relationship yang merupakan salah satu metode dengan konsep fuzzy logic. Metode tersebut memberikan tingkat akurasi yang dihitung menggunakan MAPE (Mean Absolute Percentage Error) sebesar Hasil penelitian menunjukkan bahwa peramalan pergerakan harga emas pada bulan Oktober 2020 Desember 2021 dalam rentang harga dengan harga emas tertinggi terjadi pada bulan Desember 2020.

An efficient decision support system for selecting very light business jet using CRITIC-TOPSIS method

Purpose This paper aims to assure the selection of the most suitable very light business aircraft which is preferred by the passengers based on effectiveness and aesthetic comfort. The proposed approach to determine the light business jet aircraft would provide long-range, less travel time, cozy seating arrangements, on-board lavatory facility, other aesthetic ambiance (audio systems, light systems and temperature-noise control) and appliances at reasonable flight cost. Design/methodology/approach The selection of a light business jet is obtained through multi-criteria decision-making based on the speed limit ranges from 0.57 to 0.70 Mach number and the distance traveled up to 3,000 km with the best aesthetic comfort level. To validate the approach, case studies of five aircrafts such as Honda Jet HA 420, Cessna Citation jet M2, Embraer Phenom 100, Eclipse 550 and Cessna Citation Mustang are performed. To obtain the best suitable business jet, criteria importance through intercriteria correlation (CRITIC) and technique for order performance by similarity to ideal solution (TOPSIS) is used to determine the rankings of listed aircraft. Findings The study concludes that the Cessna Citation jet M2 is chosen as the best Very Light Jet (VLJ) on the basis of speed, range, weight, cost, aesthetic and comfort. Based on the sensitivity, mean absolute percentage error (MAPE) and symmetric mean absolute percentage error analysis (sMAPE), the most and least sensitive criteria for a business jet came out to be cost and speed, respectively. Originality/value A real case study for several parameters of five different jets such as Honda Jet HA 420, Cessna Citation jet M2, Embraer Phenom 100, Eclipse 550 and Cessna Citation Mustang are shown in this paper. Based on the case study numerical values are assigned with speed, range, weight, cost, aesthetic and comfort which are applied with CRITIC and TOPSIS to obtain the most suitable business jet among the five mentioned jets which are rarely found in the literature.

Peramalan Curah Hujan Di Kabupaten Parigi Moutong Menggunakan Model Seasonal Autoregressive Integrated Moving Average (SARIMA)

Hujan merupakan fenomena alam yang sangat penting bagi kehidupan manusia. Hal ini membuat peramalan jumlah curah hujan di suatu daerah menjadi penting karena mampu mendukung proses pengambilan keputusan dalam berbagai sektor kehidupan. Dilain sisi perubahan iklim dunia membuat curah hujan seringkali susah diprediksi. Sehingga diperlukan identifikasi pola musiman pada data curah hujan sehingga mendukung peramalan curah hujan di suatu daerah. Tujuan dari penelitian ini adalah mengidentifkasi pola musiman serta menentukan model yang baik digunakan untuk meramalkan curah hujan di Kabupaten Parigi Moutong. Hasil identifikasi pola musiman mengggunakan regresi spektral menunjukkan bahwa data curah hujan di KabupatenParigi Moutong mengandung pola musiman. Selain itu diperoleh model Seasonal Autoregressive IntegratedMoving Average (ARIMA) terbaik untuk meramalkan curah hujan di Kabupaten Parigi Moutong adalah model SARIMA(1,1,0)(0,1,1)12. Model ini memiliki akurasi peramalan yang baik yang ditunjukkan dari nilai Mean Absolute Percentage Error (MAPE) sebesar 12,0157 pada data training dan 16,4647 pada data testing.

Experimental Validation of a Dynamic Photovoltaic/Thermal Collector Model in Combination with a Thermal Energy Storage Tank

The primary objective of this paper is to present a dynamic photovoltaic/thermal collector model in combination with a thermal energy storage tank. The added value of the proposed model is the use and integration of existing dynamic models for describing the entire photovoltaic/thermal system. The presented model was validated using measurements on the experimental system located at the Institute of Energy Technology, Faculty of Energy Technology, University of Maribor. The validation was carried out based on three different weather conditions—sunny, cloudy, and overcast. The validation results were evaluated using the normalized root mean square error and mean absolute percentage error for the temperature and output power of the photovoltaic/thermal collector and the temperature of the thermal energy storage tank. The model results concurred with the measurements, as the average mean absolute percentage error values for the temperature and output power of the photovoltaic/thermal collector and thermal energy storage tank temperature were 5.82%, 1.51%, and 7.58% respectively.

Generalized radiation model for human migration

One of the main problems in the study of human migration is predicting how many people will migrate from one place to another. An important model used for this problem is the radiation model for human migration, which models locations as attractors whose attractiveness is moderated by distance as well as attractiveness of neighboring locations. In the model, the measure used for attractiveness is population which is a proxy for economic opportunities and jobs. However, this may not be valid, for example, in developing countries, and fails to take into account people migrating for non-economic reasons such as quality of life. Here, we extend the radiation model to include the number of amenities (offices, schools, leisure places, etc.) as features aside from population. We find that the generalized radiation model outperforms the radiation model by as much as 10.3% relative improvement in mean absolute percentage error based on actual census data five years apart. The best performing model does not even include population information which suggests that amenities already include the information that we get from population. The generalized radiation model provides a measure of feature importance thus presenting another avenue for investigating the effect of amenities on human migration.

IMPLEENTASI MODEL HYBRID DALAM MEMPREDIKSI PENYEBARAN COVID-19 DI WILAYAH JAWA BARAT

Di awal tahun 2020, dunia dikagetkan dengan kejadian infeksi berat dengan penyebab yang belum diketahui, yang berawal dari laporan dari Cina kepada World Health Organization (WHO) terdapatnya 44 pasien pneumonia yang berat di suatu wilayah yaitu Kota Wuhan, Provinsi Hubei, China, tepatnya di akhir tahun 2019. Pada perkembangannya, wabah ini kemudian diidentifikasi sebagai wabah Virus Covid-19. Penambahan jumlah kasus COVID-19 berlangsung sangat cepat, sampai dengan 16 Februari 2020, secara global dilaporkan 51.857 kasus terkonfirmasi di 25 negara dengan 1.669 kematian (CFR 3,2%). Di Indonesia ada lebih dari 2 ribu kasus ditemukan dan hampir 200 orang telah meninggal. Di wilayah Jawa barat, kasus positif Covid-19 juga terus bertambah. Data penyebaran virus Corona Covid-19 di Jawa Barat mengalami perubahan, Rabu (1/4/2020). Terpantau melalui situs resmi Pusat Informasi dan Koordinasi Covid-19 Provinsi Jawa Barat (Pikobar), jumlah orang terpapar positif Corona mencapai 198 orang. Data yang diakses dari pikobar.jabarprov.go.id tersebut juga merilis sudah ada 11 pasien yang dinyatakan sembuh dan diperbolehkan pulang dari rumah sakit. Sementara jumlah pasien yang meninggal dunia berjumlah 21 orang. Sedangkan untuk pasien dalam pengawasan (PDP), jumlah yang telah diproses dalam pengawasan mencapai 727 orang. Sedangkan yang telah selesai menjalani pengawasan mencapai 242 orang. Total PDP di Jabar berjumlah 969 orang. Berbekal dari data Pusat Informasi dan Koordinasi Covid-19 Jawa Barat, penulis tertarik melakukan penelitian untuk memprediksi penyebaran kasus positif Covid-19 di Jawa Barat. Dalam penelitian ini, model yang digunakan adalah Hybrid. Data set yang digunakan adalah data pasien positif Covid-19 mulai bulan April 2020 sampai dengan bulan Februari 2021. Berdasarkan hasil penelitian yang sudah dilakukan, model Hybrid mampu memprediksi jumlah penyebaran kasus Covid-19 di Jawa Barat. Hal ini dibuktikan dengan hasil training teknik Hybrid memiliki error rate sebesar 0,4615, yang dilanjutkan dengan analisa akurasi prediksi selama tiga bulan, yaitu bulan Desember 2020, Januari, dan Febrauri 2021. Dari hasil prediksi, model Hybrid memiliki nilai rata – rata Mean Absolute Deviation (MAD) sebesar 351. Sedangkan berdasarkan analisa prediksi dengan teknik Mean Absolute Percentage Error (MAPE) memiliki rata rata tingkat kesalahan sebesar 0,2061 atau 20,6%.