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Fig.: Basic Representation Artificial Neural
Network(ANN) [11]](https://image.slidesharecdn.com/finalpresentationbyayana-220307123541/85/Machine-Learning-Final-presentation-21-320.jpg)
Machine Learning Final presentation
- 1. Machine Learning- Based Model to Predict the Disease Severity and Outcome inCovid-19 Patients Presented By : AYANA RUKASAR
- 2. Agenda Introduction Problem Statement Motivation Objective Work Progress WorkTo Be Done Literature Survey Research Gap Dataset Used Methodology Conclusion References
- 3. Introduction Coronaviruses are a large family of viruses that are known to cause illness ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). A novel coronavirus (COVID-19) was identified in 2019 in Wuhan, China. A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes.
- 4. Problem Statement Its 25th MarchAfternoon and India has reported its 9th death with 562 total confirmed cases due to COVID-19. Fresh cases from Manipur, Bihar, Gujrat, and Madhya Pradesh have been reported by the Union Ministry of Health and FamilyWelfare . As the coronavirus outbreak continues to spread in the country, the question that we as Indians are trying to answer is: Its 25th MarchAfternoon and India has reported its 9th death with 562 total confirmed cases due to COVID-19. Fresh cases from Manipur, Bihar, Gujrat, and Madhya Pradesh have been reported by the Union Ministry of Health and FamilyWelfare . As the coronavirus outbreak continues to spread in the country, the question that we as Indians are trying to answer is: "Will India be able to tackle this pandemic or are we going to witness another Italy/ S.Korea/Wuhan?" e able to tackle this pandemic or are we going to witness another Italy/ S.Korea/Wuhan?"
- 5. Motivation . The current COVID-19 pandemic is showing negative effects on human health as well as on social and economic life.
- 6. Objective of the Research We need a strong model that predicts how the virus could spread across different countries and regions.The goal of this task is to build a model that predicts the spread of the virus in the next 7 days.
- 7. Work Progress Literature Survey has been done. Different machine learning models has been applied and their respective accuracy has been calculated.
- 8. Work to be done Drafting of review article We will analyse the outbreak of coronavirus across various regions, visualize them using charts and graphs and predict the number of upcoming cases for the next 10 days using Linear Regression and SVM model in Python.
- 9. Literature Survey The literature survey of 5 research papers from the journal of national and international importance has been done so far and are given below.
- 10. Literature Survey Authors Journal Outbreak Infection Machine Learning Ruirui Liang Transboundary and Emerging Diseases Swine fever Random Forest R Gupta Infection Disease Modelling Dengue Classification & Regression tree (CART) Sina F. Ardabili Infectious Disease susceptible–infected– recovered (SIR) and susceptible-exposed- infectious-removed (SEIR) models Genetic algorithm (GA).
- 11. LiteratureSurvey Authors Journal Outbreak Infection Machine Learning Abbas A, Classification of COVID-19 chest X-ray images deep convolutional neural network Sethy PK Detection of coronavirus disease (COVID-19) diagnosis support vector machine
- 12. ResearchGap There are three major subfields that can be considered as future works. Improvement of the mechanism used for collection and management of datasets. Finding suitable algorithms for analysing the collected data . Investigating each major coronary artery,separately. Various ML methods and data processing techniques have been proposed for Machine Learning-Based Model to Predict the Disease Severity and Outcome in Covid-19 Patients, there are few avenues for improvement that require deep attention.
- 13. DatasetUsed Italy dataset has been used and this database contains 188 attributes out of which a subset of 14 of them has been used. Country/Region Confirmed Deaths Recovered Active New cases New Deaths New Recovered
- 14. Methodology Different machine learning models has been applied and their respective accuracy has been calculated. Random Forest SupportVector Machine Classification And Regression trees Genetic Algorithm ANN (Artificial Neutral Network)
- 15. Random Forest Algorithm Random Forest is a popular machine learning algorithm that belongs to the supervised learning technique. It can be used for both Classification and Regression problems in ML. It is based on the concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex problem and to improve the performance of the model. As the name suggests, “It is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset." Instead of relying on one decision tree, the random forest takes the prediction from each tree and based on the majority votes of predictions, and it predicts the final output. The greater number of trees in the forest leads to higher accuracy and prevents the problem of overfitting
- 17. SupportVector Machine SupportVector Machine or SVM is one of the most popular supervised learning algorithms, which is used for classification as well as regression problems. However, mainly, it is used for classification problems. The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future.This best decision boundary is called a hyperplane.
- 19. ANN (Artificial Neutral Network) Artificial neural network (ANN) is a machine learning approach that models human brain and consists of a number of artificial neurons. Neuron in ANNs tends to have fewer connections than biological neurons. Each neuron in ANN receives a number of inputs. An activation function is applied to these inputs which results in activation level of neuron (output value of the neuron). Knowledge about the learning task is given in the form of examples called training examples.
- 20. The ANN elements can be defined as: Input Layer: It consists of neurons which represents features or variables for problem solving. Output Layer: It consists of neurons which represents result of calculation in cell body. Weights: It is strength of connection between the neurons. Activation function: It is function to obtain output according problem which is to be solved. Learning Function: It is also called optimization function. It is used to obtain minimum error by updating weights. Hidden layer: It is optional & it depends on ANN architecture.
- 21. 21 Fig.: Basic Representation Artificial Neural Network(ANN) [11]
- 22. Genetic Algorithm The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions.
- 24. Classification & Regression Trees Decision Trees are an important type of algorithm for predictive modeling machine learning. The classical decision tree algorithms have been around for decades and modern variations like random forest are among the most powerful techniques available. In this post you will discover the humble decision tree algorithm known by it’s more modern name CART which stands for Classification And Regression Trees. After reading this post, you will know: • The many names used to describe the CART algorithm for machine learning. • The representation used by learned CART models that is actually stored on disk. • How a CART model can be learned from training data. • How a learned CART model can be used to make predictions on unseen data. • Additional resources that you can use to learn more about CART and related algorithms.
- 26. Tasks to be Performed Analysing the present condition in India Is this trend similar to Italy/S. Korea/ Wuhan Exploring the world wide data Forecasting the world wide COVID-19 cases using Prophet
- 27. Conclusion COVID-19 is still an unclear infectious disease, which means we can only obtain an accurate SEIR prediction after the outbreak ends.The outbreak spreads are largely influenced by each country’s policy and social responsibility.As data transparency is crucial inside the government, it is also our responsibility not to spread unverified news and to remain calm in this situation. The Machine Learning-Based Model to Predict the Disease Severity and Outcome in Covid-19 Patients project has shown the importance of information dissemination that can help in improving response time, and help planning in advance to help reduce risk. Further studies need to be done to help contain the outbreak as soon as possible.
- 28. References https://www.javatpoint.com/machine-learning-random-forest- algorithm https://www.javatpoint.com/machine-learning-classification- regression-tree-classification-algorithm https://www.javatpoint.com/support-vector-machine-algorithm- for-machine-learning https://www.javatpoint.com/artificial-neural-network
- 29. THANKYOU