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Prediction of Covid-19 patients states using Data mining techniques

Prediction of Covid-19 patients states using Data mining techniques

Introduction:

Over the past 30 years, the spread of deadly viruses has increased and spread rapidly, most recently the Coronavirus. In 2019Covid-19 is an infectious disease caused by a newly discovered coronavirus. It was first identified in several people with symptoms of pneumonia in Wuhan, Hubei Province. Given the scale of the epidemic and its rapid spread, and given that there is currently no vaccine for the virus, health care workers need support, as it can sometimes be challenging to predict the patient’s condition.

Abstract:

This problem can be solved through data mining techniques. Anticipating recovery situations is essential in countries seeking to contain the virus, and these predictions can help public health experts track positive citizens of COVID-19, increase doctors ability to predict the general perception of the course of events over a period, and assess patients at early risk building on approaches New based on results data. This paper discusses supervised learning on the COVID-19 Corona Virus India dataset in particular, which contains 3,799 patients, which used to classify the patient data of COVID-19 into two types, recovered and deceased. Classification approaches have been used, Including Decision tree (DT), Support vector machine support (SVM), Logistic regression (LR), Random forest (RF), k-nearest neighbors (KNN), Naïve Bayes (NB), and Artificial Neural Network (ANN) model in the patient dataset, and choosing the best method based on the accuracy, precision and recall withhold-out or cross-validation.

Existing work:

Manydiseases are categorized using Ensemble Data Mining techniques; these are some studies and research on this: In [14], the authors tested learning on a set of dermatological data, where dermatology was classified into six different categories and five algorithms were implemented: (CART), (SVM), (DT), (RF) and (GBDT). Use an aggregation method consisting of all five different data mining techniques as one unit. Another research in [15] applied some machine learning techniques using the Heart Disease Prediction Model with a Linear Random Hybrid Linear Forest Model (HRFLM), which the results proved its accuracy in predicting heart disease at a rate of 88.7%.

This research [9] proposed a simple model that could be useful for predicting the spread of Coronavirus depend on the Auto-Regressive Integrated Moving Average (ARIMA) on the Johns Hopkins to predict epidemiological trends.

DISADVANTAGE:

  • Difficult to understand
  • It can be time-consuming and resource intensive.

Proposed work:

This discusses supervised learning on the COVID-19 Corona Virus India dataset in particular, which contains 3,799 patients, which used to classify the patient data of COVID-19 into two types, recovered and deceased. Classification approaches have been used, Including Decision tree (DT), Support vector machine support (SVM), Logistic regression (LR), Random forest (RF), k-nearest neighbors (KNN), Naïve Bayes (NB), and Artificial Neural Network (ANN) model in the patient dataset, and choosing the best method based on the accuracy, precision and recall withhold-out or cross-validation.

Advantages:

  • Reduce time cost
  • Easy to understand

System requirements:

  Software requirements:

  • Operating system   :   Windows.
  • Coding Language  :   Python.

Hardware components:

System                   :   Pentium IV 2.4 GHz or intel

Hard Disk              :   40 GB.

Floppy Drive         :   1.44 Mb.

Mouse                    :   Optical Mouse.

Ram                       :   512 Mb.

Conclusion: The study differs from other studies in that it compared more than one classification techniques once using Hold-out and once using cross-verification and also using more than one model and comparison between them in order to choose the best model in terms of accuracy, precision, and recall of the results obtained through the algorithms including Decision tree (DT), Support Vector Machine (SVM), Logistic regression (LR), Random forest (RF), k-nearest neighbors (KNN), Naïve Bayes (NB) and Artificial Neural Network (ANN) model. The aim of the study to help public health experts to track positive citizens. From COVID- 19 and increasing physicians ’ability to anticipate public visualization to track events over a period and assess patients at risk early based on the patient’s information. As the model is now ready to enter any set of data related to information about patients and the prediction of the state. For the result even though all of them were good enough in predicting the patient data for COVID-19 using various parameters, Support Vector Machine (SVM) with Crossvalidation was found to be the best

Over the past 30 years, the spread of deadly viruses has increased and spread rapidly, most recently the Coronavirus. In 2019Covid-19 is an infectious disease caused by a newly discovered coronavirus. It was first identified in several people with symptoms of pneumonia in Wuhan, Hubei Province. Given the scale of the epidemic and its rapid spread, and given that there is currently no vaccine for the virus, health care workers need support, as it can sometimes be challenging to predict the patient’s condition.

Abstract:

This problem can be solved through data mining techniques. Anticipating recovery situations is essential in countries seeking to contain the virus, and these predictions can help public health experts track positive citizens of COVID-19, increase doctors ability to predict the general perception of the course of events over a period, and assess patients at early risk building on approaches New based on results data. This paper discusses supervised learning on the COVID-19 Corona Virus India dataset in particular, which contains 3,799 patients, which used to classify the patient data of COVID-19 into two types, recovered and deceased. Classification approaches have been used, Including Decision tree (DT), Support vector machine support (SVM), Logistic regression (LR), Random forest (RF), k-nearest neighbors (KNN), Naïve Bayes (NB), and Artificial Neural Network (ANN) model in the patient dataset, and choosing the best method based on the accuracy, precision and recall withhold-out or cross-validation.

Existing work:

Manydiseases are categorized using Ensemble Data Mining techniques; these are some studies and research on this: In [14], the authors tested learning on a set of dermatological data, where dermatology was classified into six different categories and five algorithms were implemented: (CART), (SVM), (DT), (RF) and (GBDT). Use an aggregation method consisting of all five different data mining techniques as one unit. Another research in [15] applied some machine learning techniques using the Heart Disease Prediction Model with a Linear Random Hybrid Linear Forest Model (HRFLM), which the results proved its accuracy in predicting heart disease at a rate of 88.7%.

This research [9] proposed a simple model that could be useful for predicting the spread of Coronavirus depend on the Auto-Regressive Integrated Moving Average (ARIMA) on the Johns Hopkins to predict epidemiological trends.

DISADVANTAGE:

  • Difficult to understand
  • It can be time-consuming and resource intensive.

Proposed work:

This discusses supervised learning on the COVID-19 Corona Virus India dataset in particular, which contains 3,799 patients, which used to classify the patient data of COVID-19 into two types, recovered and deceased. Classification approaches have been used, Including Decision tree (DT), Support vector machine support (SVM), Logistic regression (LR), Random forest (RF), k-nearest neighbors (KNN), Naïve Bayes (NB), and Artificial Neural Network (ANN) model in the patient dataset, and choosing the best method based on the accuracy, precision and recall withhold-out or cross-validation.

Advantages:

  • Reduce time cost
  • Easy to understand

System requirements:

  Software requirements:

  • Operating system   :   Windows.
  • Coding Language  :   Python.

Hardware components:

System                   :   Pentium IV 2.4 GHz or intel

Hard Disk              :   40 GB.

Floppy Drive         :   1.44 Mb.

Mouse                    :   Optical Mouse.

Ram                       :   512 Mb.

Conclusion: The study differs from other studies in that it compared more than one classification techniques once using Hold-out and once using cross-verification and also using more than one model and comparison between them in order to choose the best model in terms of accuracy, precision, and recall of the results obtained through the algorithms including Decision tree (DT), Support Vector Machine (SVM), Logistic regression (LR), Random forest (RF), k-nearest neighbors (KNN), Naïve Bayes (NB) and Artificial Neural Network (ANN) model. The aim of the study to help public health experts to track positive citizens. From COVID- 19 and increasing physicians ’ability to anticipate public visualization to track events over a period and assess patients at risk early based on the patient’s information. As the model is now ready to enter any set of data related to information about patients and the prediction of the state. For the result even though all of them were good enough in predicting the patient data for COVID-19 using various parameters, Support Vector Machine (SVM) with Crossvalidation was found to be the best

Introduction:

Over the past 30 years, the spread of deadly viruses has increased and spread rapidly, most recently the Coronavirus. In 2019Covid-19 is an infectious disease caused by a newly discovered coronavirus. It was first identified in several people with symptoms of pneumonia in Wuhan, Hubei Province. Given the scale of the epidemic and its rapid spread, and given that there is currently no vaccine for the virus, health care workers need support, as it can sometimes be challenging to predict the patient’s condition.

Abstract:

This problem can be solved through data mining techniques. Anticipating recovery situations is essential in countries seeking to contain the virus, and these predictions can help public health experts track positive citizens of COVID-19, increase doctors ability to predict the general perception of the course of events over a period, and assess patients at early risk building on approaches New based on results data. This paper discusses supervised learning on the COVID-19 Corona Virus India dataset in particular, which contains 3,799 patients, which used to classify the patient data of COVID-19 into two types, recovered and deceased. Classification approaches have been used, Including Decision tree (DT), Support vector machine support (SVM), Logistic regression (LR), Random forest (RF), k-nearest neighbors (KNN), Naïve Bayes (NB), and Artificial Neural Network (ANN) model in the patient dataset, and choosing the best method based on the accuracy, precision and recall withhold-out or cross-validation.

Existing work:

Manydiseases are categorized using Ensemble Data Mining techniques; these are some studies and research on this: In [14], the authors tested learning on a set of dermatological data, where dermatology was classified into six different categories and five algorithms were implemented: (CART), (SVM), (DT), (RF) and (GBDT). Use an aggregation method consisting of all five different data mining techniques as one unit. Another research in [15] applied some machine learning techniques using the Heart Disease Prediction Model with a Linear Random Hybrid Linear Forest Model (HRFLM), which the results proved its accuracy in predicting heart disease at a rate of 88.7%.

This research [9] proposed a simple model that could be useful for predicting the spread of Coronavirus depend on the Auto-Regressive Integrated Moving Average (ARIMA) on the Johns Hopkins to predict epidemiological trends.

DISADVANTAGE:

  • Difficult to understand
  • It can be time-consuming and resource intensive.

Proposed work:

This discusses supervised learning on the COVID-19 Corona Virus India dataset in particular, which contains 3,799 patients, which used to classify the patient data of COVID-19 into two types, recovered and deceased. Classification approaches have been used, Including Decision tree (DT), Support vector machine support (SVM), Logistic regression (LR), Random forest (RF), k-nearest neighbors (KNN), Naïve Bayes (NB), and Artificial Neural Network (ANN) model in the patient dataset, and choosing the best method based on the accuracy, precision and recall withhold-out or cross-validation.

Advantages:

  • Reduce time cost
  • Easy to understand

System requirements:

  Software requirements:

  • Operating system   :   Windows.
  • Coding Language  :   Python.

Hardware components:

System                   :   Pentium IV 2.4 GHz or intel

Hard Disk              :   40 GB.

Floppy Drive         :   1.44 Mb.

Mouse                    :   Optical Mouse.

Ram                       :   512 Mb.

Conclusion: The study differs from other studies in that it compared more than one classification techniques once using Hold-out and once using cross-verification and also using more than one model and comparison between them in order to choose the best model in terms of accuracy, precision, and recall of the results obtained through the algorithms including Decision tree (DT), Support Vector Machine (SVM), Logistic regression (LR), Random forest (RF), k-nearest neighbors (KNN), Naïve Bayes (NB) and Artificial Neural Network (ANN) model. The aim of the study to help public health experts to track positive citizens. From COVID- 19 and increasing physicians ’ability to anticipate public visualization to track events over a period and assess patients at risk early based on the patient’s information. As the model is now ready to enter any set of data related to information about patients and the prediction of the state. For the result even though all of them were good enough in predicting the patient data for COVID-19 using various parameters, Support Vector Machine (SVM) with Crossvalidation was found to be the best

March 14, 2022

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