• KOMAL SIDHU

The phenomenal advances in health and biotechnology have been produces huge amount of data like clinical data and high throughput information that makes Electronic Health records(EHRs) expansive and complex. For handling this AI and data mining techniques have been utilizes along with health services. Today Data mining is utilized to detect abnormal pattern using various informational datasets along with machine learning algorithms. There are many techniques available which is utilized for diagnosis of diabetes abnormal patterns like FP growth, Apriori, FP-Tree algorithm. These techniques discover unknown patterns or relationships from large amount of data and these are utilized for making decisions for preventive and suggestive medicine. The main disadvantage of these techniques is it discovers fewer patterns. In this paper we proposed prefix span along with MPV algorithm that discover more patterns to detect abnormal patterns accurately. The results will help in predicting quicker and more accurate abnormal patterns so that it lead timely treatment of the patients.

Data Mining, Diabetes Abnormal patterns Diagnosis and Treatment, Data mining techniques.

"Effective Pattern Recognition using Modified Prefix Span Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.434-440, June-2019, Available :http://www.jetir.org/papers/JETIR1906208.pdf

"Effective Pattern Recognition using Modified Prefix Span Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp434-440, June-2019, Available at : http://www.jetir.org/papers/JETIR1906208.pdf