How do you make predictions with a trained Neural Network (NAR)?]

I want to make a network that takes temperature readings and gives a prediction of the diseases caused by these values

Answer 1

[Shubham]

Hi Amna,

Creating a neural network in MATLAB to predict diseases based on temperature readings involves several steps. You'll need to prepare your data, design and train the network, and then evaluate its performance. Here's a step-by-step guide to help you get started:

Step 1: Prepare Your Data

1. Collect Data:

• Gather a dataset that includes temperature readings and corresponding disease labels. If this data is not readily available, you may need to create a synthetic dataset or find a suitable public dataset.

2. Preprocess Data:

• Normalize or standardize your temperature readings to ensure that they are on a similar scale.
• Encode disease labels into numerical format if they are categorical. MATLAB's categorical or dummyvar functions can help with this.

Step 2: Design the Neural Network

1. Choose a Network Type:

• For this task, a feedforward neural network (multilayer perceptron) is typically suitable.

2. Define the Network Architecture:

• Decide on the number of layers and neurons. A simple starting point could be one hidden layer with a few neurons.

Step 3: Implement in MATLAB

Here's a basic implementation using MATLAB's Neural Network Toolbox:

% Example temperature readings and disease labels
% This is synthetic data; replace it with your actual dataset
temperatureReadings = [98.6; 101.3; 99.1; 102.5; 97.8];
diseaseLabels = [1; 2; 1; 3; 0]; % Example labels for diseases

% Normalize the temperature readings
temperatureReadings = (temperatureReadings - min(temperatureReadings)) / ...
                      (max(temperatureReadings) - min(temperatureReadings));

% Create a feedforward neural network
hiddenLayerSize = 10;
net = feedforwardnet(hiddenLayerSize);

% Configure the neural network
net = configure(net, temperatureReadings', diseaseLabels');

% Train the network
net = train(net, temperatureReadings', diseaseLabels');

% Test the network
predictions = net(temperatureReadings');

% Display predictions
disp('Predicted Disease Labels:');
disp(round(predictions));

Step 4: Evaluate the Network

1. Assess Performance:

• Use confusion matrices, accuracy, precision, recall, and F1-score to evaluate the network's performance.
• MATLAB provides functions like confusionmat to help with this analysis.

2. Improve the Model:

• Experiment with different network architectures, learning algorithms, and hyperparameters to improve performance.
• Consider using cross-validation to ensure the model generalizes well.

Step 5: Use nntool for Visualization

• You can also use the nntool GUI in MATLAB to create, train, and visualize your neural network. This tool provides a more interactive way to handle neural networks.

nntool;

Important Considerations

• Ensure that your dataset is large and diverse enough to train a reliable model.
• Consider including additional features that might affect disease prediction, such as humidity, location, or time of year.
• Start simple and gradually increase complexity if necessary. Avoid overfitting by using techniques such as dropout or regularization.