Abstract: This study implements Case-Based Reasoning (CBR) to make the early diagnosis of cardiovascular disease based on the calculation of the feature similarity of old cases. The features used to match old cases with new ones were age, gender, risk factors and symptoms. The diagnostic process was carried out by entering the case feature into the system, and then the system searched cases having similar features with the new case (retrieve). The level of similarity of each similar case was calculated using weighted Minkowski method. Cases with the highest level of similarity would be adopted as new case solutions. If the value of similarity was <0,8, the revision would be conducted by an expert. The tests result conducted by the expert showed that the system was able to perform the diagnosis correctly. The test results are performed on the sensitivity of 100% and specificity of 83,33%. Meanwhile, the accuracy of 95,83% and the error rate of 4,17% is so that this research is relevant enough to be implemented in the medical area.

Edi Faizal and Hamdani Hamdani. “Weighted Minkowski Similarity Method with CBR for Diagnosing Cardiovascular Disease”. International Journal of Advanced Computer Science and Applications (IJACSA) 9.12 (2018). http://dx.doi.org/10.14569/IJACSA.2018.091244

@article{Faizal2018,
title = {Weighted Minkowski Similarity Method with CBR for Diagnosing Cardiovascular Disease},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.091244},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091244},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {12},
author = {Edi Faizal and Hamdani Hamdani}
}