Clinical test variables such as Hematology, Biochemistry, Serology, All Microscopy Tests etc. which will be in quantitative information
Hematology: Diagnosis related to blood and its component such as blood cells, hemoglobin, blood proteins, bone marrow, platelets, blood vessels, spleen, and the mechanism of coagulation.
Biochemistry: Biochemical tests performed on body fluids and tissues, to support diagnosis, treatment and monitoring of disease. Biochemical test such as Blood Sugar AC, S Cholesterol, Calcium, S Protein etc.
Serology: Serology refers to the diagnostic identification of antibodies that are typically formed in response to an infection. Serology test such as Mantoux Test, L E Cell test, Widal Test, STO etc.
Microscopy Tests: Microscopy Tests is identification of parasite or its antigens/products in the blood of the patient
Predicts multiple health related issues which will be in qualitative information.
We will use Multi-Layer Perceptron (MLP) and Backpropagation algorithm which will be used to train the data
However, we will concentrate on nets with units arranged in layers
Multi-Layer Perceptron Methodology works on non-linear separable data
Following are steps of algorithm
Initialize weights at random, choose a learning rate n
Until network is trained:
For each training example (input pattern and target outputs):
Present inputs for the first pattern to the input layer
Sum the weighted inputs to the next layer and calculate their activations using activation function formula
Present activations to the next layer, repeating (2) until the activations of the output layer are known
Compare output activations to the target values for the pattern and calculate deltas for the output
Propagate error backwards by using the output layer deltas to calculate the deltas for the previous layer
Use these deltas to calculate those of the previous layer, repeating until the first layer is reached
Calculate the weight changes for all weights and biases (treat biases as weights from a unit having an activation of 1)
If training by pattern, update all the weights and biases, else repeat the cycle for all patterns, summing the changes and applying at the end of the epoch
Artificial Intelligence is expanding role in modern medicine. Artificial Intelligence has huge potential to transform medical diagnosis. Implementing Artificial Intelligence in medicine has to face other challenges such as cost, resources and data. In this study we require various health related data to be analyzed to develop Artificial Intelligence System to predict health issues. It is necessary to survey the major disease types to deploy Artificial Intelligence System. Artificial Intelligence to predict health issues requires real life medical data for making better decisions