Description
Prediction and Classification of COVID-19 Death and Recovered Cases using ML
ABSTRACT:
COVID-19, Corona Virus Disease-2019, caused by a novel Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2). Effective screening of this virus can enable quick and efficient diagnosis of COVID-19 can reduce the burden on the healthcare system. Detailed analysis on the provided dataset can build different and various types of machine learning algorithms, which their performance could be computed and further evaluated. In the following case, Random Forest outperformed all the other Machine Learning models like SVR, Xgboost models. Prediction and Classification of COVID-19 Death and Recovered Cases using ML
Prediction and Classification of COVID-19 Death and Recovered Cases using ML
Overview:
The novel coronavirus (COVID-19) hit our blue planet and became an ongoing global pandemic [1]. In a little over six months since the virus was first spotted in mainland China, it has spread to more than 180 countries, infected more than 18.4 million people, and taken more than 692,000 lives as reported in the first week of August 2020. As governments and health organizations scramble to contain the spread of coronavirus, they need all the help they can get, including from artificial intelligence (AI). Though the current AI technologies are far from replicating human intelligence, they are proving to be helpful in tracking the outbreak, diagnosing patients, disinfecting areas, and speeding up the process of finding a cure for COVID-19. Forecasting is a collection of quantitative, probabilistic statements based on historical observation. It is a process of predicting unobserved events and trends. Population health monitoring and forecasting, including epidemiological outbreaks, may not have any clinical utility but can be a useful tool for planning, decision making, and prevention Prediction and Classification of COVID-19 Death and Recovered Cases using ML
Prediction and Classification of COVID-19 Death and Recovered Cases using ML
Scope of The Project:
The healthcare industry is a vast industry that requires real-time collection and processing of medical data. Moreover, at the core of this industry lies the problem of data handling which requires real-time prediction and dissemination of information to practitioners for quick medical attention. Major actors of this industry, such as physicians, vendors, hospitals, and health-based companies have attempted to collect, manage, and revive data with the aim of using it to enhance medical practices and for technological innovation. However, dealing with healthcare data has, of late, become a complex task due to the massive volume of the data, security issues, wireless network application incompetence, and the velocity at which it is increasing. Thus, to increase the efficiency, accuracy, and workflow healthcare industries need data analytics tools to manage such complex data
Prediction and Classification of COVID-19 Death and Recovered Cases using ML
System Analysis:
Existing System:
Typically, the existing mechanisms assumed that the accuracy of prediction was achieved. But this wasn?t the case then, hence, it must be improved further to increase the classification accuracy. Also, other research works addressed these issues by introducing efficient combination. Existing Models based on feature selection and classification raised some issues regarding with training dataset and Test dataset.
Dis-Advantage:
- Certain approaches being applicable only for small data.
- Certain combinations of classifiers over fit with data set while others are under fit.
- Some approaches are not adoptable for real-time collection of the database implementation.
Prediction and Classification of COVID-19 Death and Recovered Cases using ML
Proposed System:
This model is proposed with an algorithm to predict the same. Data pertaining to the study were collected from a Kaggle for which the covid-19 analysis and prediction is done, and also suitable data pre-processing methods were applied. This proposed model is also compared with other traditional classification algorithms such as Existing techniques, SVR and Random forest and XGBOOST with respect to accuracy, precision and recall.
Advantage:
- Improve the accuracy score
- Deal with large amount dataset
SOFTWARE and HARDWARE REQUIREMENTS:?
Hardware
- OS ? Windows 7, 8 and 10 (32 and 64 bit)
- RAM ? 4GB
Software
- Python
- Anaconda
Conclusion:
The application of Artificial Intelligence is very crucial to process patient data for efficient treatment strategies. In this paper we presented a model that implements the Random Forest algorithm boosted by the AdaBoost algorithm, with a F1 Score of 0.86 on the COVID-19 patient dataset. We have discovered that the Boosted Random Forest algorithm provides accurate predictions even on imbalanced datasets. The data analyzed in this study has revealed that death rates were higher amongst the Wuhan natives compared to non-natives. Also, male patients had a greater death rate compared to female patients. The majority of affected patients are aged between of 20 and 70 years.
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