• RITU VERMA
• ISHA AWASTHI

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 diseases using various informational datasets along with machine learning algorithms. There are many techniques available which is utilized for diagnosis of diabetes disease like FP growth, Apriori, spade 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 modified spade algorithm that discover more patterns to detect disease accurately. The results will help in predicting quicker and more accurate disease so that it lead timely treatment of the patients.

DATA MINING, SPADE, ACCURACY

"Disease pattern recognition using modified prefix span algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 8, page no.1019-1024, August 2019, Available :http://www.jetir.org/papers/JETIR1907741.pdf

"Disease 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 8, page no. pp1019-1024, August 2019, Available at : http://www.jetir.org/papers/JETIR1907741.pdf