Dexterous System to Find Liver Disorder and Identify the Similar Patients Using a Statistical Method

Abstract

The liver plays an active role in digestion of food, removal of toxic substances from the body and in the metabolism of carbohydrates, fats and proteins. Nowadays liver disorders have become very common all over the world due to intake of excessive alcohol, contaminated water and food. Untreated liver disorders have created a burden worldwide as they lead to some serious health problems. The mortality rate of untreated liver disorders also increased significantly in many countries. Early diagnosis of liver disorders will be very helpful to the physicians to initiate the treatment process quickly and safeguard the human society. An efficient liver disorders discovering system using machine learning algorithms will be a boon to the healthcare system to suppress the burden of liver disorders. In this work the liver disorder dataset is collected from a disease diagnostic centre and FCM algorithm is used to cluster the data into normal and liver disorders using the software developed in PHP. In this software Karl Pearson Correlation method is used to identify the most similar existing patient to a new patient for assisting the medical practitioners to prescribe best suitable medications without any human error.