Analysis of patient health condition based on hybrid machine learning algorithm

Anil N. Kumar; D. S. Vanaja; Alumuru Mahesh Reddy; Susmita Das; Sharmistha Dey; Yadala Sucharitha

doi:10.53730/ijhs.v6nS3.8358

Authors

Anil Kumar N. Assistant Professor, Department of Electronics & Instrumentation Engineering, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India
D. S. Vanaja Assistant Professor, Sri Venkatesa Perumal College of Engineering & Technology (Autonomous), Puttur, Andhra Pradesh, India
Alumuru Mahesh Reddy Assistant Professor, Sri Venkatesa Perumal College of Engineering & Technology (Autonomous), Puttur, Andhra Pradesh, India
Susmita Das Assistant Professor, Electronics and Instrumentation Engineering, Narula Institute of Technology, Kolkata, West Bengal, India
Sharmistha Dey Assistant Professor, Department of UIC, Chandigarh University, Gharuan, Punjab, India
Yadala Sucharitha Assistant Professor, Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, TS state, India

Keywords:

data mining, machine learning, health, apriori algorithm, liver, database

Abstract

In data mining, the classification methods are used to determine the relationships between the various objects of the interactions database. In this research, the objective is predominantly focused on the prediction of three types of functioning test level. The main purpose of the research work is to analyze human health condition, realised by fluctuation of specific ranges such as Bilirubin, Albumin, Prothrombin time (INR), Ascites, Encephalopathy , Bicarbonate, Calcium etc. It can improve the disease by taking medical diagnosis based on Apriori algorithm by generating rule for the most significant parameters of all three functioning test level. In this article, the experiment can be carried out by using a variety of different sizes of support count based on the association factor of attributes in the kidney and liver functional test data from a wide range of patients. The goal of the experiment is to gain an understanding of the effect that the Apriori algorithm has on the amount of time it takes for the execution, the precision of the best rule discovered from the mining of frequent patterns, and the number of association rules that are generated.

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