aparna-ranjith/mini-capstone-project

Mini Capstone Project on Covid Diagnosis- Great Learning Assessment

Mini-Capstone-Project

Mini Capstone Project on Covid Diagnosis- Great Learning Assessment

The World Health Organization has emphasised 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 faster[3]. Every health care worker exposed to the virus puts additional pressure on an already outstretched infrastructure. Here, 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. The samples were collected during a hospital visit for a suspected case of Covid-19.

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 data is anonymized and the samples were collected between 28 March - 3 April, 2020. The problem at hand is divided into two parts :

1. Predict confirmed COVID-19 cases amongst suspected cases based on the laboratory tests of their clinical samples.
2. Predict admission to general, semi-ICU, and ICU wards among those who predicted positive for COVID-19 in the first task.

Notebooks:

1. Data Cleaning -- Preprocessing the initial Dataset to remove null values
2. Prediction_Blood_Dataset -- Predicting Covid-19 in pateints on the basis of blood test attributes
3. Visualization -- EDA on the final dataset
4. Prediction_Extended_Dataset -- Predicting the occurence of Covid-19 on the basis of blood test and other attributes forming an extended dataset
5. Admission to Wards -- Predicting the admission to Regular ward, Semi-ICU, or ICU based on severity

Data

All the data used can be found in the Datasets folder.