Applications of grey prediction model for quantity prediction of medical supplies: A case study
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As an important method for modern scientific management, accurate prediction is the precondition of policy and decision making. In this paper, the metabolic sub-sequence of GM(1,1), the topological order sub-sequence of GM(1,1), and the grey series in head position sub-sequence of GM(1,1) were used to forecast the medical oxygen consumption. A rolling check method was applied to examine the prediction accuracy of the model. The result showed that the metabolic sub-sequence of GM(1,1) provided the best prediction accuracy, which may provide valuable information for optimizing purchase management and inventory control.Keywords:
Predictive modelling
Coronavirus (COVID-19) started from Wuhan, China in December 2019. Since then, this virus has affected millions of people around the world and has caused deaths in millions. As of right now, there is no cure or permanent treatment for this disease. It is well known that machine learning plays an important role in the health care system. In this research, we are going to use some machine learning models such as Decision Tree (DT), logistic regression (LR), and Random Forest (RF) for the forecasting of corona virus. These models are implemented using different machine learning libraries available in Python. This work not only serves the purpose of COVID-19 predictions using machine learning, but also attempts to find out suitable model with the best features to save time and resources. Furthermore, we also compare some of the features of different machine learning models with a deep learning model (CNN). Since healthcare environment, computational resources have to be optimized, prediction models which use less computational resources are always preferred. We believe that the outcomes of this study can help understand the performance of various predictions models in the prediction of COVID-19.
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Major League Baseball (MLB) is the highest level of professional baseball in the world and accounts for some of the most popular international sporting events. Many scholars have conducted research on predicting the outcome of MLB matches. The accuracy in predicting the results of baseball games is low. Therefore, deep learning and machine learning methods were used to build models for predicting the outcomes (win/loss) of MLB matches and investigate the differences between the models in terms of their performance. The match data of 30 teams during the 2019 MLB season with only the starting pitcher or with all pitchers in the pitcher category were collected to compare the prediction accuracy. A one-dimensional convolutional neural network (1DCNN), a traditional machine learning artificial neural network (ANN), and a support vector machine (SVM) were used to predict match outcomes with fivefold cross-validation to evaluate model performance. The highest prediction accuracies were 93.4%, 93.91%, and 93.90% with the 1DCNN, ANN, SVM models, respectively, before feature selection; after feature selection, the highest accuracies obtained were 94.18% and 94.16% with the ANN and SVM models, respectively. The prediction results obtained with the three models were similar, and the prediction accuracies were much higher than those obtained in related studies. Moreover, a 1DCNN was used for the first time for predicting the outcome of MLB matches, and it achieved a prediction accuracy similar to that achieved by machine learning methods.
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The current COVID-19 pandemic threatens human life, health, and productivity. AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates using chest x-ray. Accessing patient’s private data violates patient privacy and traditional machine learning model requires accessing or transferring whole data to train the model. In recent years, there has been increasing interest in federated machine learning, as it provides an effective solution for data privacy, centralized computation, and high computation power. In this paper, we studied the efficacy of federated learning versus traditional learning by developing two machine learning models (a federated learning model and a traditional machine learning model) using Keras and TensorFlow federated, we used a descriptive dataset and chest x-ray (CXR) images from COVID19 patients. During the model training stage, we will try to identify which factors affect model prediction accuracy and loss like activation function, model optimizer, learning rate, number of rounds, and data Size, we kept recording and plotting the model loss and prediction accuracy per each training round, to identify which factors affect the model performance, and we found that softmax activation function and SGD optimizer give better prediction accuracy and loss, changing the number of rounds and learning rate has slightly effect on model prediction accuracy and prediction loss but increasing the data size did not have any effect on model prediction accuracy and prediction loss. finally, we build a comparison between the proposed models’ loss, accuracy, and performance speed, the results demonstrate that the federated machine learning model has a better prediction accuracy and loss but higher performance time than the traditional machine learning model.
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Recently, the use of machine learning in meteorology has increased greatly. While many machine learning methods are not new, university classes on machine learning are largely unavailable to meteorology students and are not required to become a meteorologist. The lack of formal instruction has contributed to perception that machine learning methods are 'black boxes' and thus end-users are hesitant to apply the machine learning methods in their every day workflow. To reduce the opaqueness of machine learning methods and lower hesitancy towards machine learning in meteorology, this paper provides a survey of some of the most common machine learning methods. A familiar meteorological example is used to contextualize the machine learning methods while also discussing machine learning topics using plain language. The following machine learning methods are demonstrated: linear regression; logistic regression; decision trees; random forest; gradient boosted decision trees; naive Bayes; and support vector machines. Beyond discussing the different methods, the paper also contains discussions on the general machine learning process as well as best practices to enable readers to apply machine learning to their own datasets. Furthermore, all code (in the form of Jupyter notebooks and Google Colaboratory notebooks) used to make the examples in the paper is provided in an effort to catalyse the use of machine learning in meteorology.
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Machine Learning (ML) is a technology that can revolutionize the world. It is a technology based on AI (Artificial Intelligence) and can predict the outcomes using the previous algorithms without programming it. A subset of artificial intelligence is called machine learning (AI). A machine may automatically learn from data and get better at what it does thanks to machine learning. “If additional data can be gathered to help a machine perform better, it can learn. A developing technology called machine learning allows computers to learn from historical data. Machines can predict the outcomes by machine learning. For Nowadays machine learning is very important for us because it makes our work easy. to many companies are using machine learning in their products, like google is using google its google assistant, which takes our voice command and gives what do we want from it, and google is also using its goggle lens form which we can find anything just by clicking a picture, and Netflix is using machine learning for recommendation of any movies or series, Machine learning has a very deep effect on our life, like nowadays we are using selfdriving car’s.
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Machine learning is often perceived as a sophisticated technology accessible only by highly trained experts. This prevents many physicians and biologists from using this tool in their research. The goal of this paper is to eliminate this out-dated perception. We argue that the recent development of auto machine learning techniques enables biomedical researchers to quickly build competitive machine learning classifiers without requiring in-depth knowledge about the underlying algorithms. We study the case of predicting the risk of cardiovascular diseases. To support our claim, we compare auto machine learning techniques against a graduate student using several important metrics, including the total amounts of time required for building machine learning models and the final classification accuracies on unseen test datasets. In particular, the graduate student manually builds multiple machine learning classifiers and tunes their parameters for one month using scikit-learn library, which is a popular machine learning library to obtain ones that perform best on two given, publicly available datasets. We run an auto machine learning library called auto-sklearn on the same datasets. Our experiments find that automatic machine learning takes 1 h to produce classifiers that perform better than the ones built by the graduate student in one month. More importantly, building this classifier only requires a few lines of standard code. Our findings are expected to change the way physicians see machine learning and encourage wide adoption of Artificial Intelligence (AI) techniques in clinical domains.
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In recent years, the advancement of artificial intelligence (AI) and the progress of machine intelligence has allowed the people to perceive the great future of AI in the healthcare field. Deep learning technology has shown the promising results in early disease prediction. The performance of multi disease prediction has been improved dramatically due to progressive development from machine learning to deep learning technology. The most difficult task is accurate and early disease prediction. It aims to demonstrate the significant relationship between deep learning and healthcare industry mainly for early disease prediction. In this paper, deep learning based multi disease prediction such as diabetes, breast cancer and covid 19 detection are proposed and analysed. The selected deep learning models in this paper were ANN and CNN. These networks were chosen, as they contain only less number of layers than complex architectures like Densenet and Resnet model. Kaggle datasets are used for all three different diseases for efficient detection. The performance of deep learning classification algorithms is evaluated using a variety of evaluation metrics such as accuracy, precision, sensitivity and specificity. Our obtained results shows that ANN and deep CNN model achieves higher accuracy than existing machine learning models. Our proposed model has shown the greater accuracy of 73.37%, 96.49%, 96.66% in diabetes, breast cancer and covid-19 disease detection.
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