Similarity analysis of patients' data: Bangladesh perspective

Misspelling of names is a major problem of real world datasets and a single person is identified differently as its consequence. In Bangladesh, it is common that many people, in real, do not know their full name and many of Bangladeshi citizens are unable to pronounce their name correctly, even in the mother tongue. The Same person provides a different version of their name during taking a public service e.g., treatment in hospital. In almost all healthcare centers, a patient is asked and he reports his demographic data i.e. name, age, etc. orally. This creates ambiguity with misspelled names. In this paper, we have provided an algorithm to identify the same person correctly from the variation of names. Experimental results show that our proposed technique can successfully link records with high accuracy for noisy data like misspelled patient names etc.