This document presents the findings of a study that developed a machine learning model to predict whether a person has COVID-19 based on their symptoms. It describes building neural network and other models using a dataset of symptoms from COVID-19 patients. The models were tested using various validation techniques, with the neural network achieving over 99% accuracy. Future work proposed taking additional clinical data to improve accuracy and developing a real-time online system to demonstrate the prediction model.
2. Introduction
01
2
Agenda Corona Prediction from Symptoms
State of the Art
02
Methodology / System Diagram
03
Training & Testing Corpus
04
Result & Discussion
05
Future Work
06
Real Time System Demonstration
07
Conclusion
08
4. Coronavirus & Prevention
“Coronavirus Disease- 2019”, is a respiratory illness caused by the
severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).
● First reported to affect human life in Wuhan City, in the
Hubei province of China in December 2019
● Later, spread like wildfire throughout the rest of the world,
marking its presence in 235 countries & independent
territories with confirmed death of 9,67,164 [1]
● Immediate Countermeasures are highly need to curb this
catastrophic effects
Algorithms
That Can Be Used To Solve This Problem
● Similarity Based Algorithm
● Machine Learning Algorithm
● Deep Learning Algorithm
Introduction
4
Corona Prediction from Symptoms
5. Prediction Model
● We have worked on corona symptom dataset
● We predict knowledge from user's symptoms using
different ML/DM Techniques
Why Prediction is Important ?
5
Corona Prediction from Symptoms
6. State of the Art
Presenting By
Dipongker Sen (012191010)
Parvin Akter (012193013)
6
7. State of the Art
State-of-the-art (SoTA) is a step to demonstrate the novelty of your
research results.
● Less work is being done on and predicting using text data.
● The major problem in the identification of COVID-19 is
detection and diagnosis.
● Different prediction models are built using machine learning
algorithms
Recent Studies on COVID Data
Data Types Algorithm Findings
Blood test report and
symptoms
CNN, LR Accuracy:
CNN (77%), LR (71%)
Based on age group [24] Random Forest Regressor
Random Forest Classifier
Accuracy: 96.6%
CT images [22] Neural Network Accuracy: 93.9%
Textual clinical data [23] Naïve Bayesian
Classification
Accuracy: 96.2%
Precision: 94%
Recall: 96%
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Corona Prediction from Symptoms
8. Applied Algorithms on COVID– 19 Based on Textual Data
Data Types Applied Algorithms Findings
Textual Clinical Data labeled with COVID,
ARDS, SARS and Both (COVID, ARDS)
** 212 Textual Report of Patients with
symptoms
● Logistic regression and
Multinomial Naïve Bayesian
Algorithm
● SVM
● Decision Tree
Accuracy: (LR ) and (MN) 94% precision,
96% recall, f1 score 95% and accuracy
96.2%.
Covid-19 regular symptoms
** 67,161 covid -19 patient
ML models based algorithm like
● SVM
● KNN+NCA
● Decision Tree Classifier
● Multilinear Regression
● Logistic Regression
● Random Forest Classifier
● XGBoost Classifier
Regressor and Random Forest Classifier
has outperformed other models in terms
of Coefficient of Determination (0.97 and
0.92) and accuracy 0.966%.
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Corona Prediction from Symptoms
18. VS
Sigmoid in Feedforward
SoftMax in Backpropagation
Sigmoid in Both
Feedforward & Backpropagation
Findings: Sigmoid Performs Better
✔ Obtains the best accuracy within less epoch.
✔ Error rate is also much lower.
Multi-Layer Neural Network: Accuracy
MLNN
Architecture
Total Data : 1000
Learning Rate, η : 0.05
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Corona Prediction from Symptoms
19. SoftMax & Sigmoid
VS
Sigmoid in Feedforward
SoftMax in Backpropagation
Sigmoid in Both Feedforward &
Backpropagation
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MLNN: Predicted Result on Sample Inputs
'Fever', 'Tiredness', 'Dry-Cough', 'Difficulty-in-Breathing', 'Sore-Throat', 'Pains', 'Nasal-Congestion', 'Runny-Nose', 'Diarrhea'
20
MLNN
Architecture
Total Data : 1000
Learning Rate, η : 0.05
Corona Prediction from Symptoms
27. 2
Statistical analysis on
user’s testing in our
system.
3
Take clinical symptoms
(Pulse Rate, Oxygen Level,
Blood Pressure, Chest X-
Ray, CT scan) to provide
better and reliable
accuracy.
4
Develop our algorithm
(modified version) and publish a
research paper.
1
Apply our system on real
data.
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Future Work Corona Prediction from Symptoms
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References Corona Prediction from Symptoms