• Ashok kumar
• velayudham

Abstract- Dimensionality Reduction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality. Dimensionality reduction techniques offer solutions that both significantly improve the computation time, and yield reasonably accurate clustering results in high dimensional data. Data mining technique called clustering is involved for medical data due to its high dimensionality and data sparsity. We proposed a Modern approach to capture and render vast quantities of clinical data during the health care process. Utilization of data analysis and data mining methods in medicine and health care is sparse. Medical data is one of the heavily used categorical type data. We proposed an algorithm can be very useful for reduce the possible symptoms of large medical and health care database to determining the best matching symptoms for the health disorders. Our objective is to search for clinically meaningful clusters of chronic illness, rather than clusters of patients. The results show that the clustering based symptoms observed approach is very efficient in terms of fast and timely decision making for medical representatives to help on time for the illness emergency patients.

dimensionality reduction , medical data

"A Novel Approach for Dimension Reduction in High Dimensional Medical Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 5, page no.287-297, May-2018, Available :http://www.jetir.org/papers/JETIR1805471.pdf

"A Novel Approach for Dimension Reduction in High Dimensional Medical Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 5, page no. pp287-297, May-2018, Available at : http://www.jetir.org/papers/JETIR1805471.pdf