Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (20)
Similaire à A Machine Learning Approach to Predicting Covid-19 Cases Amongst Suspected Cases and Their Category of Admission
Similaire à A Machine Learning Approach to Predicting Covid-19 Cases Amongst Suspected Cases and Their Category of Admission (20)
Dernier
Dernier (20)
A Machine Learning Approach to Predicting Covid-19 Cases Amongst Suspected Cases and Their Category of Admission
- 1. A MACHINE LEARNING APPROACH TO PREDICTING COVID-19 CASES AMONGST SUSPECTED CASES AND THEIR CATEGORY OF ADMISSION Dr. D Narayana, Amanpreet Singh, Aparna Ranjith, Divya Dixit, Abhay, Suparna Khan, Anwesh Reddy Presented by Amanpreet Singh
- 2. COVID-19 timeline DEC’19 First documented Covid-19 hospital admission in Wuhan, Hubei JAN ‘20 Multiple cases registered from countries around the globe. FEB ‘20 WHO named the disease Coronavirus Disease - 2019 (COVID-19) MAR ‘20 UN global response launched by WHO. Complete lockdown imposed in several countries APR ‘20 Evidence of transmission from asymptomatic to symptomatic and pre-asymptomatic people infected with Covid-19 were reported
- 4. Objective ● Predict confirmed COVID-19 cases amongst suspected cases based on the laboratory tests of their clinical samples. ● Predict admission to general, semi-ICU, and ICU wards among those who predicted positive for COVID-19 in the first task. The World Health Organization has emphasized the need for comprehensive testing in order to fight the virus . With the lack of testing kits available worldwide, there is a call for novel testing methods that can help arrest the spread effectively.
- 5. About the data Source Hospital Israelita Albert Einstein, at São Paulo, Brazil Date Range The data has been collected between March 28 - April 3, 2020 Total records 5644 Attributes 111 Data points used for prediction Subset I Subset II Attributes: 20 Attributes:48 Records: 598 Records: 254 Standard Deviation 1 Mean 0 Challenges Missing values and imbalanced data
- 6. Clinical Parameters ● Complete Blood Count Tests ● Liver Function Tests ● Renal Analysis (Kidney) ● Influenza Tests ● Blood gas analysis ● Salt tests
- 7. Logical Grouping of Attributes Blood Liver Function Influenza Renal(Urine) Haemoglobin Alanine Transaminase RSV Urine-ph Red Blood Cells Aspartate Transaminase Influenza A Urine-Aspect Lymphocytes Total Bilirubin Influenza B Urine-Color C-Reactive Protein Indirect Bilirubin Rhinovirus/Enteroviru s Urine-RBC Creatinine Alkaline Phosphatase Adenovirus Urine-Density Potassium GGT Parainfluenza 3 Urine-Haemoglobin
- 9. Ward Distribution of Patients Of all the positive patients ● 1.4% were admitted to ICU ● 1.4% were admitted to Semi-ICU ● 6.5% were admitted to Regular Ward
- 10. Methodology ● Data Cleaning ● Imbalanced Data – Sampling(UpSampling and Downsampling) ● Imputation ● Classification ● Results
- 11. Feature Selection Criteria The first subset has been made taking the features related to CBC along with age quantile and three categorical variables representing the hospital admission and the target variable (‘sars_cov2’). The information in these features is related to RBC, WBC and platelets. A total of 20 features have been chosen with 598 records that have no missing values. The second subset includes features related to blood, flu and liver parameters along with age quantile and three categorical variables representing the hospital admission and the target variable (‘sars_cov2’). It contains 48 attributes with 254 records. The larger dataset after careful filtering had missing values which were eventually treated using a placeholder value. For the ward prediction task, the ICU, semi-icu, regular ward were merged into one categorical column with ordinal values 0,1,2,3 01 02 03
- 12. RESULTS - AUC/ROC Metrics
- 14. Model Performances Model Performances Model Training Accuracy(%) Testing Accuracy(%) Logistic Regression 91.52 89.61 Decision Tree 90.39 83.11 Random Forest 97.17 94.80 Bagging 100 96.10 Gradient Boost 100 94.80 ADA Boosting 98.30 93.50
- 15. Conclusion ● Our results demonstrate that adding more bio-markers can help in increasing prediction of covid-19 using machine learning approach. ● Further it can help in predicting severity of the disease (ward). ● Adding these methods to testing protocols can minimize contact for healthcare workers. ● The model’s output can be used as a tool for prioritization and to support further medical decision-making processes.
- 16. Future Enhancements ● Larger Dataset ● More ethnically representative sample data ● Capturing critical features like D-dimer, CRP ● Including X-ray ● Considering co-morbidities
- 17. Thank You
Notes de l'éditeur
- Hello everyone, my name is Amanpreet Singh In this study we propose a machine learning approach towards predicting Covid-19 cases among a sample population who have undergone other clinical tests and blood spectrum tests.
- Before moving forward lets talk a little about the covid-19 –December ‘19 - First documented COVID-19 patient was admitted in the hospital in Wuhan, Hubei Province, China –January ‘20 - Multiple cases registered from countries around the globe and WHO declared the COVID-19 outbreak as the sixth public health emergency of international concern –February ‘20 - WHO named the disease caused by 2019-nCoV: Coronavirus Disease-2019 (COVID-19) –March ‘20 - The UN Global Humanitarian Response Plan was launched by the WHO. Complete Lockdown was introduced in many countries. –April ‘20 – Evidence of transmission from symptomatic, pre-symptomatic and asymptomatic people infected with COVID-19 were reported. WHO published a draft landscape of COVID-19 candidate vaccines
- Developing countries with fewer testing resources have adopted conservative testing methods in order to conserve testing kits on symptomatic and mild to severe cases. As more evidence surfaces around symptoms and associated risks of Covid-19, it has been observed that blood examination can play a key role in the route to extensive testing.
- The patient data used in this effort has been donated by Hospital Israelita Albert Einstein, at São Paulo, Brazil for the purpose of research. The blood and clinical samples were collected of patients who visited the hospital for a suspected infection of Covid. The data was made available in a standardized and normalized form. It has a unit standard deviation and a mean of zero There was an imbalance in class distribution across both target columns ‘sars_cov2’ and ‘Ward’
- The features which comprise various clinical parameters can be broadly categorized under CBC, liver function, renal analysis, salt tests, blood gas analysis (arterial and venous), And influenza tests. The CBC is indicative of an individual’s overall health and detects a range of potential disorders but is not limited to anaemia, leukaemia, or inflammation
- The attributes which have more than 98% of missing values were eliminated. After Data Cleaning, it was observed that there was an imbalance in class distribution across both target columns ‘sars_cov2’ and ‘Ward’ so we have performed sampling there, both up sampling and down sampling. The missing data was not imputed as imputations may render the results compromised. Binary classification is used in the first problem statement. And in the second problem we have used multi-class classification. In the results we have used auc/roc metrics with prediction accuracy of our model performances.
- Scenario-1: Area under the ROC-curve for Covid-19 Predictions was observed to be 87.58%. Scenario-2: Area under the ROC-curve for Covid-19 Predictions was observed to be 97.82%.
- Prediction accuracy of the model is also an important factor in determining the efficiency of the model. we are able to predict with 87.0 - 97.4 percent accuracy at a 95 percent confidence level that a patient is suffering from Covid-19 When biomarkers are taken into consideration. Among those that tested positive, we were able to demonstrate that our model could predict with 87.0 - 100 percent accuracy at a 95 percent confidence that whether the patient would be admitted to a particular ward.
- We performed classification using different machine learning algorithms and compared the testing and training accuracy of the different classifiers used. The best accuracy was obtained using Random Forest Classifier with a training accuracy of 97.17% and testing accuracy of 94.80 % .