Trying to input values and determine the class of the value

How can I input a set of glucose values for example and let my network return the class (healthy or diabetic)

clc; clear all; close all;
load Projectdata.mat

% Split Glucose Data for training 
GR_output=categorical(GR_output);
%Transposing the data 
GlucoseReadings_T = GlucoseReadings';
%Renamin the transposed data for training 
GlucoseReadings_train = GlucoseReadings_T;
%Segmenting the data for training 
train_GlucoseReadings = GlucoseReadings_train(1:84,:);
%Output for segmented data
train_GR_output = GR_output(1:84);

%Split Insulin Data for training 
INS_output=categorical(INS_output);
InsulinReadings_T = InsulinReadings';
InsulinReadings_train = InsulinReadings_T;
train_InsulinReadings = InsulinReadings_train(1:84,:);
train_INS_output = INS_output(1:84);

% Split glucose data for validation
GlucoseReadingsTrain=(reshape(train_GlucoseReadings', [1749,84]));
val_GlucoseReadings = GlucoseReadings_train(85:102,:);
val_GR_output = GR_output(85:102);
GlucoseReadingsVal=(reshape(val_GlucoseReadings', [1749,18]));
%Split glucose data for testing
test_GlucoseReadings =GlucoseReadings_train(103:120,:);
test_GR_output = GR_output(103:120);
GlucoseReadingsTest=(reshape(test_GlucoseReadings', [1749,18]));
numFeatures = size(GlucoseReadings_T,2);

%Splitting Insulin data for validation
InsulinReadingsTrain=(reshape(train_InsulinReadings', [1758,84]));
val_InsulinReadings = InsulinReadings_train(85:102,:);
val_INS_output = INS_output(85:102);
InsulinReadingsVal=(reshape(val_InsulinReadings', [1758,18]));
%Split Insulin data for testing
test_InsulinReadings = InsulinReadings_train(103:120,:);
test_INS_output = INS_output(103:120);
InsulinReadingsTest=(reshape(test_InsulinReadings', [1758,18]));
numFeatures1 = size(InsulinReadings_T,2);

% number of hidden units represent the size of the data
numHiddenUnits = 120;
%number of classes represents the different variations(normal and diabetic)
numClasses = length(categories(categorical(GR_output)));
numClasses1 = length(categories(categorical(INS_output)));
layers = [ ...
    sequenceInputLayer(numFeatures) 
    dropoutLayer(0.5)
    %instanceNormalizationLayer
    bilstmLayer(round(numHiddenUnits/2),'OutputMode','sequence')
    fullyConnectedLayer(numClasses)
    %instanceNormalizationLayer
    dropoutLayer(0.5)
    softmaxLayer
    classificationLayer];
layers1 = [ ...
    sequenceInputLayer(numFeatures1) 
    dropoutLayer(0.5)
    %instanceNormalizationLayer
    bilstmLayer(round(numHiddenUnits/2),'OutputMode','sequence')
    fullyConnectedLayer(numClasses)
    %instanceNormalizationLayer
    dropoutLayer(0.5)
    softmaxLayer
    classificationLayer];
options = trainingOptions('adam', ...
    'MaxEpochs',200, ...
    'GradientThreshold',1, ...
    'Verbose',false, ...
    'ValidationData',{GlucoseReadingsVal, val_GR_output},...
    'LearnRateDropFactor',0.4,...
    'LearnRateDropPeriod',5,...
    'Plots','training-progress');
options1 = trainingOptions('adam', ...
    'MaxEpochs',200, ...
    'GradientThreshold',1, ...
    'Verbose',false, ...
    'ValidationData',{InsulinReadingsVal, val_INS_output},...
    'LearnRateDropFactor',0.4 ,...
    'LearnRateDropPeriod',5,...
    'Plots','training-progress');

% Train
% whos
net = trainNetwork(GlucoseReadingsTrain,train_GR_output,layers,options);
net1 = trainNetwork(InsulinReadingsTrain,train_INS_output,layers1,options1);
% Test
miniBatchSize = 27;
GR_outputPred = classify(net,GlucoseReadingsTest,...
    'MiniBatchSize',miniBatchSize,...
    'ExecutionEnvironment','cpu');
acc = mean(GR_outputPred(:) == categorical(test_GR_output(:)))
INS_outputPred = classify(net1,InsulinReadingsTest,...
    'MiniBatchSize',miniBatchSize,...
    'ExecutionEnvironment','cpu');
acc1 = mean(INS_outputPred(:) == categorical(test_INS_output(:)))
figure
t = confusionchart(categorical(test_GR_output(:)),GR_outputPred(:));
figure
t1 = confusionchart(categorical(test_INS_output(:)),INS_outputPred(:));

[Nathaniel Porter]

The data used to train network

Answer 1

[yanqi liu]

clc; clear all; close all;
load Projectdata.mat

% make class to healthy or diabetic
for i = 1 : length(GR_output)
    if ~isempty(strfind(lower(GR_output(i)), 'no'))
        GR_output(i) = 'healthy';
    else
        GR_output(i) = 'diabetic';
    end
end
for i = 1 : length(INS_output)
    if ~isempty(strfind(lower(INS_output(i)), 'no'))
        INS_output(i) = 'healthy';
    else
        INS_output(i) = 'diabetic';
    end
end

% Split Glucose Data for training 
GR_output=categorical(GR_output);
%Transposing the data 
GlucoseReadings_T = GlucoseReadings';
%Renamin the transposed data for training 
GlucoseReadings_train = GlucoseReadings_T;
%Segmenting the data for training 
train_GlucoseReadings = GlucoseReadings_train(1:84,:);
%Output for segmented data
train_GR_output = GR_output(1:84);

%Split Insulin Data for training 
INS_output=categorical(INS_output);
InsulinReadings_T = InsulinReadings';
InsulinReadings_train = InsulinReadings_T;
train_InsulinReadings = InsulinReadings_train(1:84,:);
train_INS_output = INS_output(1:84);

% Split glucose data for validation
GlucoseReadingsTrain=(reshape(train_GlucoseReadings', [1749,84]));
val_GlucoseReadings = GlucoseReadings_train(85:102,:);
val_GR_output = GR_output(85:102);
GlucoseReadingsVal=(reshape(val_GlucoseReadings', [1749,18]));
%Split glucose data for testing
test_GlucoseReadings =GlucoseReadings_train(103:120,:);
test_GR_output = GR_output(103:120);
GlucoseReadingsTest=(reshape(test_GlucoseReadings', [1749,18]));
numFeatures = size(GlucoseReadings_T,2);

%Splitting Insulin data for validation
InsulinReadingsTrain=(reshape(train_InsulinReadings', [1758,84]));
val_InsulinReadings = InsulinReadings_train(85:102,:);
val_INS_output = INS_output(85:102);
InsulinReadingsVal=(reshape(val_InsulinReadings', [1758,18]));
%Split Insulin data for testing
test_InsulinReadings = InsulinReadings_train(103:120,:);
test_INS_output = INS_output(103:120);
InsulinReadingsTest=(reshape(test_InsulinReadings', [1758,18]));
numFeatures1 = size(InsulinReadings_T,2);

% number of hidden units represent the size of the data
numHiddenUnits = 120;
%number of classes represents the different variations(normal and diabetic)
numClasses = length(categories(categorical(GR_output)));
numClasses1 = length(categories(categorical(INS_output)));
layers = [ ...
    sequenceInputLayer(numFeatures) 
    dropoutLayer(0.5)
    %instanceNormalizationLayer
    bilstmLayer(round(numHiddenUnits/2),'OutputMode','sequence')
    fullyConnectedLayer(numClasses)
    %instanceNormalizationLayer
    dropoutLayer(0.5)
    softmaxLayer
    classificationLayer];
layers1 = [ ...
    sequenceInputLayer(numFeatures1) 
    dropoutLayer(0.5)
    %instanceNormalizationLayer
    bilstmLayer(round(numHiddenUnits/2),'OutputMode','sequence')
    fullyConnectedLayer(numClasses)
    %instanceNormalizationLayer
    dropoutLayer(0.5)
    softmaxLayer
    classificationLayer];
options = trainingOptions('adam', ...
    'MaxEpochs',200, ...
    'GradientThreshold',1, ...
    'Verbose',false, ...
    'ValidationData',{GlucoseReadingsVal, val_GR_output},...
    'LearnRateDropFactor',0.4,...
    'LearnRateDropPeriod',5,...
    'Plots','training-progress');
options1 = trainingOptions('adam', ...
    'MaxEpochs',200, ...
    'GradientThreshold',1, ...
    'Verbose',false, ...
    'ValidationData',{InsulinReadingsVal, val_INS_output},...
    'LearnRateDropFactor',0.4 ,...
    'LearnRateDropPeriod',5,...
    'Plots','training-progress');

% Train
% whos
net = trainNetwork(GlucoseReadingsTrain,train_GR_output,layers,options);
net1 = trainNetwork(InsulinReadingsTrain,train_INS_output,layers1,options1);
% Test
miniBatchSize = 27;
GR_outputPred = classify(net,GlucoseReadingsTest,...
    'MiniBatchSize',miniBatchSize,...
    'ExecutionEnvironment','cpu');
acc = mean(GR_outputPred(:) == categorical(test_GR_output(:)))
INS_outputPred = classify(net1,InsulinReadingsTest,...
    'MiniBatchSize',miniBatchSize,...
    'ExecutionEnvironment','cpu');
acc1 = mean(INS_outputPred(:) == categorical(test_INS_output(:)))
figure
t = confusionchart(categorical(test_GR_output(:)),GR_outputPred(:));
figure
t1 = confusionchart(categorical(test_INS_output(:)),INS_outputPred(:));


% test demo
GR_outputPred_demo = classify(net,GlucoseReadingsTest(:,1),...
    'ExecutionEnvironment','cpu')
INS_outputPred_demo = classify(net1,InsulinReadingsTest(:,10),...
    'ExecutionEnvironment','cpu')

GR_outputPred_demo =

categorical

healthy

INS_outputPred_demo =

categorical

diabetic