Key result
Data mining models identify key clinical indices to aid early diagnosis of heart disease.
Why the study?
Early and timely diagnosis of heart disease is crucial for preventing its progression and reducing treatment costs, but data mining techniques yield varying results across datasets.
Data mining models for heart disease diagnosis remain hypothesis-generating; prospective validation needed before clinical adoption.
In recent decades, heart disease has been identified as the leading cause of death across the world. However, it is considered as the most preventable and controllable disease at the same time. According to World Health Organization (WHO), the early and timely diagnosis of heart disease plays a remarkable role in preventing its progress and reducing related treatment costs. Considering the ever-increasing growth of heart disease-induced fatalities, researchers have adopted different data mining techniques to diagnose it. According to results, application of the same data mining techniques leads to different results in different datasets. This study tries to assist healthcare specialists to early diagnose heart disease and assess related risk factors. To this end, the main heart disease diagnosis indices were identified using experts’ opinions. Then, data mining techniques were applied on a heartrelated dataset. Finally, the main heart disease diagnosis indices were identified and a model was developed based on extracted rules. Visual Studio was used to write the algorithm code.
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Assari et al. (2017) studied Heart disease. Data mining techniques was evaluated. Data mining techniques were applied to a heart-related dataset to identify key diagnosis indices and develop a model for the early diagnosis of heart disease.
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