Knowledge Discovery from Medical Data and Development of an Expert System in Immunology.

Jan Mulawka,Irina Mikutskaya,Małgorzata Pac

doi:10.3390/e23060695

Abstract

Artificial intelligence is one of the fastest-developing areas of science that covers a remarkably wide range of problems to be solved. It has found practical application in many areas of human activity, also in medicine. One of the directions of cooperation between computer science and medicine is to assist in diagnosing and proposing treatment methods with the use of IT tools. This study is the result of collaboration with the Children’s Memorial Health Institute in Warsaw, from where a database containing information about patients suffering from Bruton’s disease was made available. This is a rare disorder, difficult to detect in the first months of life. It is estimated that one in 70,000 to 90,000 children will develop Bruton’s disease. But even these few cases need detailed attention from doctors. Based on the data contained in the database, data mining was performed. During this process, knowledge was discovered that was presented in a way that is understandable to the user, in the form of decision trees. The best models obtained were used for the implementation of expert systems. Based on the data introduced by the user, the system conducts expertise and determines the severity of the course of the disease or the severity of the mutation. The CLIPS language was used for developing the expert system. Then, using this language, software was developed producing six expert systems. In the next step, experimental verification was performed, which confirmed the correctness of the developed systems.

Highlights

IntroductionLow awareness of some rare diseases and lack of experience pertaining to their scope result in delays in terms of their diagnosis and treatment [1]
The main aim of the article is to discover knowledge from the obtained base and establish an expert system based on this knowledge
The following packages used in this article for exploring data: Rpart—a package used for determining decision trees rpart.plot—a package used for visualization of decision trees randomForest—a package used for analyzing and building models based on random forests ggplot2—a visualization package ggpubr—a visualization package plyr—a data manipulation package

Summary

Introduction

Low awareness of some rare diseases and lack of experience pertaining to their scope result in delays in terms of their diagnosis and treatment [1]. Patients often waste their valuable time waiting for a doctor’s appointment. The use of computer techniques is intended to help in diagnosing and proposing treatment methods [3]. It enables detection of dependencies in huge medical databases, which are subsequently used for treating and predicting the patient’s status in many clinical settings. The use of innovative technologies, AI techniques in medical applications, can reduce costs, time and medical errors

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Discussion

Conclusion