Problem is Related to Healthcare Applications of AI Algorithms - Report Writing - IT Assignment Help

Assignment Task

Heart Disease Classification Using Deep Neural Networks
The problem is related to healthcare applications of AI algorithms. The classification goal is to predict whether the patient presents heart disease or not. The target class is the last attribute (Heart disease) and has two values (0 = no, 1 = yes). You should split the data into 70% as the training set and 30% as the testing set.
Attribute Information:
1. age: The person's age in years
2. sex: The person's sex (1 = male, 0 = female)
3. cp: The chest pain experienced (Value 1: typical angina, Value 2: atypical angina, Value 3: nonanginal pain, Value 4: asymptomatic)
4. trestbps: The person's resting blood pressure (mm Hg on admission to the hospital)
5. chol: The person's cholesterol measurement in mg/dl
6. fbs: The person's fasting blood sugar (> 120 mg/dl, 1 = true; 0 = false)
7. restecg: Resting electrocardiographic measurement (0 = normal, 1 = having ST-T wave abnormality, 2 = showing probable or definite left ventricular hypertrophy by Estes criteria)
8. thalach: The persons maximum heart rate achieved
9. exang: Exercise induced angina (1 = yes; 0 = no)
10. oldpeak: ST depression induced by exercise relative to rest ('ST' relates to positions on the ECG plot. See more here)
11. slope: the slope of the peak exercise ST segment (Value 1: upsloping, Value 2: flat, Value 3: downsloping)
12. ca: The number of major vessels (0-3)
13. thal: A blood disorder called thalassemia (3 = normal; 6 = fixed defect; 7 = reversable defect)
14. target: Heart disease (0 = no, 1 = yes)

Steps
The project involves the following steps:
1. Data exploration: try to know data and represents statistics for the important features among the features and the target attribute.
2. Use Neural Network and Deep Learning to build a classifier. Use Neural Network algorithms provided by TensorFlow to train a model based on training examples. You should try different architectures (e.g., layers, nodes) to achieve the best results.
3. Test the learned model on the test set and report the testing results in terms of different parameters such as accuracy, AUC, output error.
4. Parameter sensitivity analysis: report results in terms of accuracy when tuning the parameters.

For example, presents your observation when you change different parameters such as:
1. Change number of epochs
2. Change number of neurons
3. Use different activation functions

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