Why predictive research is needed?4 answersPredictive research is needed for several reasons. Firstly, it helps in improving the interpretation of research results and enhances the credibility of findings. Secondly, predictive analytics play a crucial role in theory building and theory testing, alongside explanatory modeling. Additionally, predictive research allows for the identification of promising research early on, aiding in determining which research deserves investment. Moreover, predictive analytics assist in the development of more accurate and general-purpose models, capturing actual research trends from various directions. Overall, predictive research is necessary for assessing predictive power, improving experimental design, and predicting future research trends, thereby adding theoretical and practical value to research.
How does predictive analytics work?3 answersPredictive analytics works by analyzing historical data using statistical algorithms, machine learning techniques, and artificial intelligence to identify patterns and relationships in the data. These patterns are then used to make predictions about future outcomes. The goal is to go beyond simply knowing what has happened in the past and provide a best assessment of what will happen in the future. Predictive analytics can be used in various industries such as banking, healthcare, retail, and manufacturing to reduce risk, optimize operations, and increase revenue. It involves techniques like data mining, regression analysis, decision trees, and neural networks to develop models that can predict values for new or different data. In the context of stock market analysis, predictive analytics can be used to forecast trends and make buying or selling decisions based on calculated values and models like Moving Average (MA) and Long Short Term Memory (LSTM).
What meaning of prediction in this journal?5 answersPrediction in the context of the journal refers to the task of forecasting the future performance of academic journals. This includes predicting the number of citations a journal will receive during the next calendar year and predicting the Elsevier CiteScore a journal will be assigned for the next calendar year. The authors propose the use of neural network models trained on historical bibliometric data to make these predictions. They perform feature selection and model configuration for a Multi-Layer Perceptron and a Long Short-Term Memory, and demonstrate superior performance compared to heuristic prediction baselines and classical machine learning models.
What are the main factors that predict the outcome of hantavirus infection?5 answersThe main factors that predict the outcome of hantavirus infection include the presence of alien species as potential biocontrol agents, environmental conditions, harvesting strategy, small mammal assemblages, phylogenetic relatedness among host species, genetic factors such as human leukocyte antigen (HLA) alleles, autoimmune phenomena, Epstein-Barr virus infection, and various biomarkers.These factors can influence the spread and severity of hantavirus infections, ranging from inapparent subclinical infection to severe hemorrhagic fever with renal syndrome (HFRS). Age, gender, humoral immune response, clinical and laboratory findings, transfusion, mechanical ventilation requirement, and antiviral treatment are also prognostically important.Understanding these factors can help predict hantavirus disease outbreaks and guide treatment approaches.
How can machine learning be used to predict disease outcomes?3 answersMachine learning techniques, such as Random Forest, Support Vector Machines, and Naive Bayes, have been used to predict disease outcomes. These algorithms have shown promising results in disease prediction, with Random Forest algorithm performing the best with an average accuracy of 87%. The performance of the Random Forest model can be further improved through tuning, resulting in an accuracy of 90%. Machine learning models can be trained using input variables such as age, glucose level, and other factors. These models are then used to predict the presence or absence of specific health conditions based on new input data. By incorporating machine learning and artificial intelligence techniques, disease prediction systems can help in early detection of diseases and decrease mortality rates. These prediction systems can assist doctors in accurately identifying diseases in less time.
What are the reasons why it is necessary to predict mortality?3 answersPredicting mortality is necessary for several reasons. Firstly, it is an important responsibility of physicians to determine a patient's prognosis. Secondly, accurate mortality prediction models are crucial in the field of cardiac surgery to assess the risk of death and improve patient outcomes. Thirdly, in the case of Covid-19, mortality rate prediction helps in understanding the progression of the outbreak, informing government actions, and reducing infection numbers and mortality rates. Lastly, mortality rate modeling and forecasting allow for the determination of fundamental characteristics of a population, providing valuable insights for healthcare planning and resource allocation.