Abstract
Nowadays, due to change in lifestyle, the problem of heart disease has become very common. When patients visit hospitals for nominal reason, they have to undergo different tests suggested by a doctor which create lot of stress in patients leading to loss of money as well as time. Since doctors suggest number of tests to patients to identify the problem, there are chances that few tests may not be required at preliminary stage. Also poor clinical decisions may lead to some disastrous conditions. In existing systems, all the features are tested at a time by the classifier in order to detect whether patient is suffering from that particular disease or not. The entire feature testing consumes a lot of time. Also if system is testing all attributes of a healthy person, then it is wastage of time. So the proposed idea is that the attributes/features are decimated into groups according to their importance. These groups of features are then input to different stages of multiple classifier system. If output of stage I is showing risk of disease, then only system will go for second stage of classifier with second-level attribute set as input and so on. Thus, for healthy person system will stop at first stage with conclusion no risk of disease. In this way, multiple classifier system utilizes time efficiently. Simultaneously, patient is also relieved from unnecessary stress as well as fatigue. Calculations for basic architecture of neural network show that time complexity in terms of number of additions and multiplication is reduced by 58 and 30%, respectively, for assumed case.
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Tajanpure, R.R., Jena, S. (2018). Diagnosis of Disease Using Feature Decimation with Multiple Classifier System. In: Dash, S., Das, S., Panigrahi, B. (eds) International Conference on Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 632. Springer, Singapore. https://doi.org/10.1007/978-981-10-5520-1_7
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DOI: https://doi.org/10.1007/978-981-10-5520-1_7
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