Developing a Software for Diagnosing Heart Disease via Data Mining Techniques

Yaser AbdulAali JASIM, Mustafa G. SAEED


This paper builds a data mining tool via a classification method using Multi-Layer Perceptron (MLP) with Backpropagation learning method and an algorithm of feature selection along with biomedical testing values for diagnosing heart disease. Addition to that, developing a prototype for heart disease diagnosing with a friendly-user graphical interface (GUI). The purpose to construct this software is that; clinical prosopopoeia is done in any event by doctor’s experience. Despite that, some cases are reported negative diagnosis and treatment; therefore, patients are asked to take a number of tests for diagnosis. Moreover, not all the tests contribute towards an effective diagnosis of a disease, and by using data mining approach to diagnose heart disease that supports the doctors to make more efficient and subtle decisions.


Data mining, Artificial Neural Network, Matlab R2016a and Heart disease

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