A Novel Analysis of Diabetes Mellitus by Using Expert System Based on Brain Derived Neurotrophic Factor (BDNF) Levels

Author Name: *Dharmaiah Devarapalli, Allam Apparao, Amit Kumar, G R Sridhar
Author Email: devarapalli.dharma@gmail.com

Abstract

In this paper a novel concept of designing and building intelligent expert system for the detection and diagnosis of Diabetes Mellitus is introduced. The expert system classification is based on critical diabetic parameters like Brain-Derived Neurotrophic Factor (BDNF) levels, and Fasting Blood Glucose (FBG). The proposed rule-based expert system constructs large-scale knowledge-base based on the behavior of the BDNF related diabetic data. The system will give an expert decision taking into consideration all the valid ranges of diabetic parameters. The proposed expert system can work effectively even for large sets of patient data.

Keywords

BDNF, FBG, Type2 diabetes mellitus, expert system, and rule based system.

Introduction

Expert or knowledge-based systems are the commonest type of AI systems in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusions. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules [1]. Rule based system can be used to develop the knowledge base used by expert systems. Given a set of clinical cases that acts as examples, rule-based system can produce a systematic description of those clinical features that uniquely characterize the clinical condition. This knowledge can be expressed in the form of simple rules or often as decision tree. Figure 1 shows the concept of a knowledge based expert system. The expert system receives facts from the user and provides expertise in return. The two main components of an expert system (invisible from outside) are the knowledge base and inference engine. [2] Knowledge Base: It contains the domain knowledge coded in the form of knowledge. It stores all relevant information, data, rules, cases and relationships used by the expert system. The knowledge base can combine the knowledge of multiple human experts. A knowledge base is the nucleus of an expert system. A knowledge base is not a data base. The knowledge base is created by the knowledge engineers who translate the knowledge of real human experts into rules. In a rule based expert system a knowledge base is usually stored in terms of if-then rules. The knowledge base of expert systems contains both factual and heuristic knowledge. Factual knowledge is that knowledge of the task domain that is widely shared, typically found in textbooks or journals, and commonly agreed upon by those knowledgeable in the particular field. Heuristic knowledge is the less rigorous, more experiential, more judgmental knowledge of performance. In contrast to factual knowledge, heuristic knowledge is rarely discussed, and is largely individualistic. It is the knowledge of good practice, good judgment, and plausible reasoning in the field. For the difficult problems with which expert system are concerned it uses heuristics: Strategies that often lead to correct solution, but that also sometime fail.
Inference Engine: The Inference engine may infer solutions or conclusions from the knowledge base, based on the facts supplied by the user. The inference engine acts as an interpreter or scheduler that interprets which rules matches with the facts. The word inference or reasoning is very important in expert system; because reasoning is the standard technique by which expert system solve problems. It makes inferences by deciding which rules are satisfied by facts, prioritizes the satisfied rules, and execute the rule with the highest priority. These conclusions are the expert system’s response to the user queries, as the user supplies facts or other information to the expert system for getting the expertise or expert advice. So the purpose of inference engine to seek information and relationship from the knowledge base and to provide answers, predictions and suggestions in the way a human expert would. The inference engine must find the right facts, interpretations and rules and assemble them correctly. Two types of inference methods are commonly used.

Conclusion

The proposed expert system based on critical diabetic parameters like BDNF (brain-derived Neurotrophic factor) levels, and FBG (Fasting Blood glucose) is found to be effective in diagnosis of diabetic mellitus clinical condition. Patient data of south Indian states was considered for experimental validation of the proposed system. The rule based expert system determines the diabetic condition based on the high or low values of BDNF levels along with other relevant clinical parameters for diabetic mellitus condition.

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