Rule-Based Medical Decision-Making with Learning (original) (raw)

Rule Based Expert System for Medical Diagnosis-A Review

2016

Traditionally human experts were responsible for taking decisions in solving the medical problems. But it was very difficult for human expert to solve complex problems,for that expert systems were designed.There are lot of applications in artificial intelligence domain that try to help human experts offering solutions for a problem. Expert system is a part of artificial intelligence which increases the ability of decision making of the human expert. They are designed to solve complex problems.Rule based expert system uses rules as the knowledge representation for knowledge coded into the system. Rule based expert system is used by the human experts to diagnose the problem.This paper surveys research work accomplished in the field of medical sector using rule based expert system.

A Rule-based Approach for Medical Decision Support

This paper describes the medical Decision Support System (DSS) designed in the framework of the Bravehealth (BVH) project. The DSS is the heart of the data processing performed in Bravehealth, and it is aimed at enriching the medical experience to support the doctors in the decisionmaking processes. The paper focuses on the flexible and effective DSS architecture placed at a Remote Server side. Moreover, a Data Mining prototype algorithm, supported by the architecture, is proposed, along with encouraging test results.

Comparison of Rule-Based and Bayesian Network Approaches in Medical Diagnostic Systems

Lecture Notes in Computer Science, 2001

Almost two decades after the introduction of probabilistic expert systems, their theoretical status, practical use, and experiences are matching those of rule-based expert systems. Since both types of systems are in wide use, it is more than ever important to understand their advantages and drawbacks. We describe a study in which we compare rule-based systems to systems based on Bayesian networks. We present two expert systems for diagnosis of liver disorders that served as the inspiration and vehicle of our study and discuss problems related to knowledge engineering using the two approaches. We finally present the results of a simple experiment comparing the diagnostic performance of each of the systems on a subset of their domain.

A Novel Fuzzy Logic-Based Medical Expert System for Diagnosis of Chronic Kidney Disease

Mobile Information Systems

Chronic kidney disease is a life-threatening complication. Primary diagnosis and active control avoid its progression. To increase the life span of a patient, it is necessary to detect such diseases in early stages. In this research paper, design and development of a fuzzy expert system (FES) to identify the current stage of chronic kidney disease is proposed. The proposed fuzzy rule-based expert system is developed with the help of clinical practice guidelines, database, and the knowledge of a team of specialists. It makes use of input variables like nephron functionality, blood sugar, diastolic blood pressure, systolic blood pressure, age, body mass index (BMI), and smoke. The normality tests are applied on different input parameters. The input variables, i.e., nephron functionality, blood sugar, and BMI have more impact on the chronic kidney disease as shown by the response of surface analysis. The output of the system shows the current stage of patient’s kidney disease. Totally ...

Rule based diagnosis system for diabetes

Biomedical Research-tokyo, 2017

In this time of chaotic era, where world is widely affected by diseases such as diabetes, there exists a need for an expert system which can predict diabetes at the very early stages with minimum of fuss and in a time efficient manner. The system should be efficient enough to forecast whether a person is suffering from diabetes or not, with the ability to predict the probability among various types of diabetic types like type-1, type-2, pre-diabetes, and gestational, with which the patient is suffering. The developed system is influenced by various evident factors collected from plethora of sources such as Physicians, books, Internet, medical journals etc. The variety of factors acts as an indicator and serves the basis of rule formation. Rules formations are further engineered with the help of modern techniques like Fuzzy Logic to create an Inference engine. User input is passed to the inference engine, which subsequently produces the output in terms of diabetes type with its proba...

Medical Decision Support Tool from a Fuzzy-Rules Driven Bayesian Network

Proceedings of the 10th International Conference on Agents and Artificial Intelligence, 2018

The task of carrying out an effective and efficient decision on medical domain is a complex one, since a lot of uncertainty and vagueness is involved. Fuzzy logic and probabilistic methods for handling uncertain and imprecise data both provide an advance towards the goal of constructing an intelligent decision support system (DSS) for medical diagnosis and therapy. This work reports on a successfully developed DSS concerning pneumonia disease. A detailed and clear description of the reasoning behind the core decision making module of the DSS, is included, depicting the proposed methodological issues. The results have shown that the suggested methodology for constructing bayesian networks (BNs) from fuzzy rules gives a front-end decision about the severity of pulmonary infections, providing similar results to those obtained with physicians' intuition.

Rule Generation and Evaluation by Data Mining Ensembles for Clinical Decision Support

Biomedical Engineering, 2013

Clinical decision support systems (CDSS) often base on rules that are inferred from collected patients' histories, together with expert judgements and consented medical guidelines. This type of advisor system is known as rulebased reasoning system or expert system which classifies a given test instance into a particular outcome from the learned rules. The test instance carries multiple attributes which are usually the values of diagnostic tests. In this paper, we propose a classifier ensemble-based method for supporting disease diagnosis. The ensemble data mining learning methods are applied for rule generation, and a multi-criteria evaluation approach is used for selecting reliable rules over the results of the ensemble methods. The efficacy of the proposed methodology is illustrated via an example of a thyroid disease classification.

A Fuzzy-Mining Approach for Solving Rule Based Expert System Unwieldiness in Medical Domain

Over the years, one of the challenges of a rule based expert system is the possibility of evolving a compact and consistent knowledge-base with a fewer numbers of rules that are relevant to the application domain, in order to enhance the comprehensibility of the expert system. In this paper, the hybrid of fuzzy rule mining interestingness measures and fuzzy expert system is exploited as a means of solving the problem of unwieldiness and maintenance complication in the rule based expert system. This negatively increases the knowledge-base space complexity and reduces rule access rate which impedes system response time. To validate this concept, the Coronary Heart Disease risk ratio determination is used as the case study. Results of fuzzy expert system with a fewer numbers of rules and fuzzy expert system with a large numbers of rules are presented for comparison. Moreover, the effect of fuzzy linguistic variable risk ratio is investigated. This makes the expert system recommendation close to human perception.

A STUDY OF EXPERT SYSTEM FOR DETECTION OF VARIOUS DISEASES

IJIRIS :: AM Publications,India, 2020

In the field of medical regularly handles enormous amounts of data. Handling huge data by conventional methods can affect the results. Algorithms for machine learning can be used to find out facts in medical research, in particular for disease prediction. Expert System with artificial intelligence technology, Data mining technology has also been extensively developed, which has promoted the development of for detection and diagnosis of different diseases. The purpose this study aims to know the detection of brain, heart, kidney etc with the help of different algorithms such as Feedforward Backpropagation, Support Vector Machine, Generalized Regression Neural Network, Radial Basis Function and association rules in data mining. This study introduces the above neural network techniques in detail and experiments on medical data collected from hospital. The experimental results show that the some neural network techniques will detect the disease based on medical data collected from hospital and recommend medicine. Applying neural network techniques and data mining techniques based medical data disease detection has greatly improved the level of medical detections and understandings.

RULE LEARNING OVER MEDICAL DATA WITH MACHINE LEARNING ALGORITHMS

In this paper, OneR, Navie Bayes, JRip, Ridor, SMO, J48, LMT, Conjunctive Rule, Decision Tables, NNge, KStar, IBk, PART machine learning algorithms and Fuzzy Logic with classification analysis made from instances in medical data set. Furthermore, JRip, PART, OneR algorithms and Fuzzy Logic with constituted rules. Machine learning is all about learning rules from data. OneR algorithm is used in fuzzy logic classification to confirm. In this classification L_O2 and ADM_DECS attributes used. Approach to OneR algorithm is limited value 95.5 of L_O2. L_O2(A) and L_O2(S) fuzzy membership functions are approximately intersect at point ≈ 96.07 from the fuzzy membership functions at figure 1. This value is accepted as limit value. Each of our two results is nearer to each other. And we can see that results calculated with different fuzzy membership functions are more sensitive than the result of OneR algorithm.