search

rawat28 / Hear-disease-prediction-Using-PSO

Heart Disease Prediction: Implemented a neural network model in Jupyter Notebook using TensorFlow and Keras for heart disease prediction. Utilized PSO for hyperparameter optimization, achieving accurate classification results.
0 stars 0 forks source link

A question about "Hear-disease-prediction-Using-PSO" #1

Open WenJieh616 opened 2 weeks ago

WenJieh616 commented 2 weeks ago

Hello rawat28,

I have read the Hear-disease-prediction-Using-PSO (https://github.com/rawat28/Hear-disease-prediction-Using-PSO) that you uploaded to Github, and I have a question for you. It is an example of classification, can you upload another example for regression problems? Thank you!

WenJieh616 commented 2 weeks ago

Please give me an answer, thank you.

rawat28 commented 2 weeks ago

Please give me an answer. Thank you.

Hi @WenJieh616, I appreciate you taking the time to look over my work.

In the context of the same heart disease prediction code/project, an example of a regression problem could be predicting the risk score or probability of heart disease rather than simply classifying whether an individual has heart disease or not. Instead of predicting a binary outcome (presence or absence of heart disease), we could modify the project to predict a continuous value, such as a risk score ranging from 0 to 1, representing the probability or severity of heart disease. To achieve this, we must make a few significant changes to a number of different components, including the target variable, loss function, and output layer.

Output Layer: Change the output layer of the neural network to have a single neuron with a linear activation function (instead of a sigmoid function), as this will generate a continuous value.

Loss Function: We have to use a regression loss function such as mean squared error (MSE) or mean absolute error (MAE) instead of binary cross-entropy.

Target Variable: Instead of a binary target variable (0 or 1), our target would be a continuous value representing the risk score, which could be derived from existing medical data or created by combining multiple risk factors.

I will demonstrate a few sample code modifications for you:

Change the output layer for regression

model.add(Dense(1, activation='linear')) # For regression, use 'linear' activation

Compile the model with a regression loss function

model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=['mae'])

Please do not hesitate to get in touch with me if you have any questions or would like the complete code.