Comparative Study On SVM And LUSI For Classification Of Chronic Diseases

The current world is fighting for several serious issues including the individual’s worth of life in human society.The care in terms of clinical perspectives needs some important information including access to relevant data.Which can in turn be helpful in the proper and timely diagnosis of certain diseases.Several machine learning tools have been used for this purpose but failed,the failure include various reasons including lack of proper data,disproportionality,majority and minority classes,or the use of different improper invariants to analyze the data.However,the lack of such data or the ups and downs in the data provided makes it quite difficult to gain successful diagnosis and classification which can cost the life of innocents across the world.The case of various distinct disorders was the subject of the current investigation including Heart,Diabetes,Kidney,and Parkinson’s by using the machine learning technique,which is an emerging technology updating day by day to overcome such problems.To overcome this problem,a set of properly rebalanced algorithmic model LUSI(Learning using rebalanced statistical invariant constructed with appropriate predicates and corresponding invariants)was evaluated in comparison to other machine learning tools with LSSVM.The result was further processed through kernel tests.The result performance was evaluated based on F-Measures,G-Means and average accuracy including features like specificity and sensitivity in percentage.The obtained average accuracy rate was 99.65,74.26,99.82 & 92.31 for Heart,Diabetes,Kidney and Parkinson’s disease respectively.The result obtained through LUSI invariants was comparatively high from LSSVM used in this study,which is 99.60,64.88,72.65 and 72.65 for the diseases considered in this study.The result concluded that the LUSI uniform consist of most properly used invariant with significant data.The reason behind it is the combination of features in the construction of Algorithmic model.Which is capable of estimating data proportionality in perfection.So,the current study concluded the use of LUSI uniform for the proper diagnosis of various different diseases including,Heart,Diabetes,Kidney and Parkinson’s diseases.