Fuzzy Expert System to Calculate the Strength/ Immunity of a Human Body

Transcription

1 Indian Journal of Science and Technology, Vol 9(), DOI: /ijst/16/v9i/101, November 16 ISSN (Print) : ISSN (Online) : Fuzzy Expert System to Calculate the Strength/ Immunity of a Human Body Ranjit Kaur, Vishu Madaan and Prateek Agrawal* School of Computer Science Engineering, Lovely Professional University, Phagwara - 111, Punjab, India; reetbansal09@gmail.com, vishumadaan123@gmail.com, prateek061186@gmail.com Abstract Objectives: Determining the level of strength of patients body is essential for an Ayurvedic Physician to provide the appropriate dose of medicines to them. The proposed work is a stepping stone towards a vigilant and proper diagnosis that will surely leads to an efficacious treatment. Methods/Statistical Analysis: A fuzzy logic based expert system is designed to calculate the quality of the seven tissues (Lymph, Blood, Muscle, Fat tissue, Bone, Bone Marrow, Reproductive tissues) and mind of a human body. Based on that quality the overall body strength is computed. All the required parameters are finalized after consulting an Ayurvedic Expert during knowledge acquisition phase. Findings: The accuracy and veracity of the system is evaluated by ranking all results of different defuzzification methods. Data collection is performed by survey method and results were highlighted as false positives and false negatives after verifying from expert. Maximum results depict that the outputs produced by the proposed system matches with those of Ayurvedic Expert. Application/ Improvements: The proposed system will guide the physicians to plan the dose of medicines and treatment for the patients. This fuzzy based system will prove itself as a learning kit for Ayurveda and Vedic practitioner. Keywords: Defuzzification, Examination of Strength, Fuzzy Expert System, Fuzzy Logic, Fuzzification, Inference System 1. Introduction Ayurveda is an ancient science of healing diseases of individuals. Ayurveda is a blend of two words Ayur which signifies life and Veda which signifies science. Therefore the term Ayurveda connotes Science of Life. The origin of Ayurveda is native to the Indian subcontinent and was discovered thousands of years ago. Ayurveda says that every individual is different from each other and hence the prescription for every individual should be personalized 1. Therefore, Ayurveda considers not only the examination of diseases but also focuses on the examination of the patients as well. One of the concepts of examination of patients in Ayurveda is Ten Fold Examination 2. These ten investigations regarding the patients are: Prakritipariksha (Examination of Human Constituents) 3. Vikritipariksha (Examination of morbidity). Saar pariksha (Examination of Strength of Body). Samhananapariksha (Examination of compactness). Pramanapariksha (Examination of body measurements). Satmyapariksha (Examination of homologation). Satvapariksha (Examination of mental constitution). Aharshaktipariksha( Examination of digestive capacity). Vyayamshaktipariksha (Examination of capacity for exercise). Vayapariksha (Examination in respect of age). The prime focus of this research work is the Saar Pariksha (Examination of Strength of body) as it is practiced for the assessment of the biological strength of an *Author for correspondence

2 Fuzzy Expert System to Calculate the Strength/Immunity of a Human Body individual or power of resistance against the diseases. It includes the examination of vitality and strength of seven tissues namely, plasma, blood, bone, bone marrow, fat, muscle, reproductive tissues. In addition to these tissues strength of mind is also been considered. 1.1 Importance of Saar Pariksha (Examination of Strength) One of the principal contributors to the science of Ayurveda-Charaka has accentuated on the fact that during the examination of patient, the physicians can misapprehend and make an inaccurate decision by merely looking at the body of the patient such as strong because of being stout and chubby, weak because of being slim and skinny, very weak because of short and small body. But it is been observed that contrary to this can happen such as some individuals with small and slim body can be strong enough. These misconceptions of physicians can lead to prescribing inappropriate dose of medicines. Therefore, there is a need of development of a system which could automatically and accurately compute the strength of the patients. 2. Design of Proposed System The proposed system is designed using the fuzzy logic toolbox in MATLAB. All the parameters for computing the quality of tissues and mind are acquired by conferring the experts. Intuition method is used to select the Membership Functions for each of the factors which are then fuzzified and a pertinent knowledge base is constructed 6. For implementing the system standard Figure 1. Design of proposed expert system for calculating strength of body. 2 Vol 9 () November 16 Indian Journal of Science and Technology

3 Ranjit Kaur, Vishu Madaan and Prateek Agrawal Max-min Mamdani inference process has been used. The system is defuzzified by applying the centroid method. The complete design of the proposed system for calculating the strength of the human body is outlined in the following Figure 1. A total of 9 fuzzy systems are constructed for calculating the strength. The inputs provided to Fuzzy System-1, Fuzzy System-2, Fuzzy System-3, Fuzzy System-, Fuzzy System-, Fuzzy System-6, Fuzzy System-7 and Fuzzy System-8 are the parameters considered for determining the quality of lymph blood, muscle, fat tissues, bone, bone marrow, reproductive tissues and mind respectively. The output of these eight fuzzy systems acts as the input to the Fuzzy System-9 which will finally calculate the overall strength or immunity of the patient as shown in Figure Identification of the Parameters The first and foremost step for the development of the fuzzy expert system is to identify the parameters which will assist to determine the quality of seven tissues (lymph, blood, bone, bone marrow, fat tissues, muscles, reproductive tissues) plus the quality of mind. These Parameters are gathered through consulting an Ayurvedic doctor as well from the Ayurvedic books 7,8. The identified parameters for each tissue are shown in Table 1. Table 1. Parameters for evaluating quality of tissues Tissues Plasma Blood Bone Bone Marrow Fat Muscle Reproductive Tissues Signs of Strength of Tissue Soft, smooth and shiny skin small, delicate and deep rooted body hairs Shiny and reddish eyes, lips, tongue, face, forehead, palms, foot sole and sexual organs Big and firm heel, ankle, knee joint, collar bones, jaw/chin, head, nails and teeth Smooth and clear complexion, smooth voice, big joints, thin body Soft and smooth hairs, nails, teeth, smooth voice, viscous urine, greasy stool Stable, bulky and healthy mass on forehead, eyes, jaws, shoulder, abdomen, nape of neck, armpit, chest and cheeks Cheerful and positive nature, smooth and equal sized teeth, smooth voice, glowing skin, heavy buttocks the crisp inputs into fuzzy membership values 9. All the input variables have two trapezoidal and one triangular membership function whereas the output variables have two triangular and one trapezoidal membership function. The triangular and trapezoidal membership function can be defined by following Equation 1and Equation 2 respectively 10. The fuzzy sets of input and output variable and their ranges for Fuzzy System-9 are described in Table 2. The membership functions for input and output variables are shown in Figure 3 and Figure. Figure 3. Membership function for input variables. Figure 2. Block diagram of proposed system. 2.2 Fuzzification After identifying the parameters, the next step is to fuzzily these input values. Fuzzification is a way of converting Figure. Membership function for output variables. Vol 9 () November 16 Indian Journal of Science and Technology 3

4 Fuzzy Expert System to Calculate the Strength/Immunity of a Human Body Table 2. Membership function ranges for fuzzy linguistic input and output variable Membership function ranges of Fuzzy Linguistic Input variables(0-10) Membership function ranges of Fuzzy Linguistic Output variables(0-10) Strength or immunity of patient LOW MEDIUM HIGH POOR MODERATE EXCELLENT of Lymph of Blood of Bone of Bone Marrow of Fat of Muscle of Reproductive Tissues of Mind Figure. Fuzzy rule base. Figure 6. Surface view of rule base. 2.3 Fuzzy Rule Formation Next step after the fuzzification is to construct the fuzzy rules. The rules constructed for the proposed system are in IF-THEN format introduced by in 11. IF part is called the antecedent whereas the THEN part is called the consequent 12. The IF-THEN rules are represented as: IF (condition 1 AND condition 2 AND condition n) THEN (do something) Fuzzy rules form the knowledge base of a fuzzy expert system. Fuzzy expert system makes the decisions based on these rules. Fuzzy rules for the proposed system are constructed by consulting the Ayurvedic expert. Some of the sample rules for determining the strength of human body are shown: IF (Lymph is High) AND (Blood is ) AND (Muscle is Low) AND (Fat tissue is Low ) AND (Bone is High) AND (Bone Marrow is Low) AND (Mind is High)AND (Reproductive tissue is ) THEN (Strength is Moderate) IF (Lymph is ) AND (Blood is ) AND (Muscle is ) AND (Fat tissue is Low ) AND (Bone is Low) AND (Bone Marrow is ) AND (Mind is Low) Vol 9 () November 16 Indian Journal of Science and Technology

5 Ranjit Kaur, Vishu Madaan and Prateek Agrawal Table 3. Defuzzified values and ranks for different inputs Inputs ( of seven tissues and Mind) Defuzzified values and ings LYMPH BLOOD MUSCLE BONE BONE MARROW FAT TISSUES REPRODUCTIVE- TISSUE MIND CENTROID BISECTOR MOM SOM LOM AND (Reproductive tissue is ) THEN (Strength is ) There are 336, 2, 2, 190, 31, 81, 23 and 23 rules formed for Fuzzy System-1, Fuzzy System-2, Fuzzy System-3, Fuzzy System-, Fuzzy System-, Fuzzy System-6, Fuzzy System-7, Fuzzy System-8 respectively. Figure and Figure 6 shows the fuzzy rules and surface view of the rule base for Fuzzy System-9 which is calculating the overall strength of the body. 2. Fuzzy Inference Generation For the proposed system Mamdani Inference system will be used as shown in Figure 7. Mamdani s fuzzy inference process is the widely used fuzzy methodology. There are chances that for the same output membership function many rules will be active and selection of only a single membership value is necessary. Minimum input value is Figure 7. Mamdani Inference System for calculating strength of body. Vol 9 () November 16 Indian Journal of Science and Technology

6 Fuzzy Expert System to Calculate the Strength/Immunity of a Human Body Table. Sample test results for fuzzy inference system for calculating strength of body S.No. INPUT OUTPUT RES- ULT Blood Lymph Muscle Fat Tissue Bone Bone Marrow Reproductive tissue Mind Strength (Given by Fuzzy System) Strength (Given by Expert) Excellent False Excellent picked up if an AND operation is used between inputs when that rule will be active. Similarly the maximum input value is selected when an OR operation is used between inputs and its membership value will be evaluated as membership value of the output for that rule. This is known as implication method. This process is repeated for every rule for determining the output membership functions. After implication method, in the next step fuzzy sets that portray the outputs of every rule are combined into a single fuzzy set which is referred as the aggregation process. The list of truncated output functions given by the implication process for every rule becomes the input Figure 8. User Interface for calculating strength of human body. of the aggregation process. For every output variable the output of the aggregation process is a single fuzzy set. This process given by in is represented through following equation: 6 Vol 9 () November 16 Indian Journal of Science and Technology

7 Ranjit Kaur, Vishu Madaan and Prateek Agrawal 2. Deffuzification After the fuzzy inference process, a single crisp output value is obtained by defuzzifying the aggregated fuzzy set 13. Five of the built in defuzzification methods in Matlab are LOM, SOM, MOM, Centroid and bisector. The proposed system is defuzzified using the Centroid method. It can be calculated as: Table 3 shows the inputs given to the Fuzzy System-9 in which different defuzzification techniques are used to obtain the output for each input set and ranks are given to them 1. For example, the highest output computed using centroid method can be given rank 1; the second highest output computed using centroid method will be given rank 2 and so on. Similarly the ranking method is applied to all the other eight Fuzzy Systems. 2.6 Assessing and Evaluating Results The accuracy and veracity of the proposed system can be evaluated by comparing the outputs produced by the system with the results given by the expert. A survey is done and data of 12 patients were collected. The proposed fuzzy expert system is then tested to check whether the output generated by the system matches with the answers of Ayurvedic expert. Table shows test results for the sample data of patients. As the maximum values obtained through the system matches with the expert s opinion, this shows the accuracy of results produced by the system. 2.7 User System Interaction User interface for the system is developed such that an excel sheet of inputs for calculating the quality of seven tissues is imported and displayed on the click of Browse button as shown in Figure 8. Excel sheet contains all the factors for calculating the quality of tissues and mind. The user has to enter the values from 1 to 10 in the excel sheet. For example, for skin type user can enter 1 to 3 if his skin is rough, to 6 if his skin is normal or 7 to 10 if his skin is oily. Computed values of quality of all seven tissues and mind are displayed. Based on the values of quality of seven tissues and mind, the strength of body is also computed and displayed. 3. Discussion and Conclusion The proposed system is developed to calculate the strength/immunity of a human body. As during the examination of patient, the physicians can misapprehend and make an inaccurate decision by merely looking at the body of the patient; this system can facilitate the physicians to determine the strength or immunity of the individual with more veracity which could help the physicians to prescribe the appropriate dose of medicine to their patients. Therefore, this system could be used as a tool which can assist the Ayurvedic doctors as well as a learning and educative system for the Ayurvedic Medical students. Also even a layman who doesn t have any knowledge of Artificial intelligence can use this system to check his/her body strength. This research work can further be extended by considering more factors that are responsible for evaluating the quality of the tissues. Also this system could be developed as an application based on Neuro-Fuzzy technique.. Acknowledgements The authors want to acknowledge the expert Dr. Rabjyot Kaur who works as Ayurvedic Medical Officer at Government Ayurvedic Dispensary (GAD), Bombeli, Hoshiarpur and Punjab, India for her continuous support while development and testing of this system.. References 1. A statistical fuzzy inference system for classifying human constituents. 10. Available from: org/document/7163/ 2. Shashirekha HK, Bargale SS. Importance of dasha vidha pareeksha in clinical practice. Journal of Ayurveda and Holistic Medicine. 1 Apr; 2(3): An approach to develop Multi Techniques Integrated Expert System for Diagnosis of Human Constitutions. 08. Available from: Mandip RG. Applied aspects of dasha-vidha atura pariksha. Journal of Ayurveda Physicians Surgeons. 1 Mar; 2(2):0-6.. Agrawal P, Vishu, Kumar V. Fuzzy rule-based medical expert system to identify the disorders of eyes, ENT and liver. International Journal of Advanced Intelligence Paradigm. 1 Dec; 7(3/): Vol 9 () November 16 Indian Journal of Science and Technology 7

8 Fuzzy Expert System to Calculate the Strength/Immunity of a Human Body 6. Sivanandam SN, Sumathi S, Deepa SN. Introduction to Fuzzy Logic using MATLAB. 1 st ed. Springer; Mehta AK, Gupta N, Sharma RN. Jain B. Health and Harmonythrough Ayurveda Sharma R, Dash B. Charaka Samhita. Varanasi, India: Chowkhamba Sanskrit Series; Ross T. Fuzzy logic with engineering applications. 3rd ed. John Wiley and Sons; Fuzzy Rule based Students Performance Analysis Expert System. 1. Available from: document/678129/ 11. Darlington K. The essence of expert system. 3 rd ed. Prentice Hall; Jackson P. Introduction to Expert Systems. 3 rd ed. USA: Addison-Wesley; A computer model for diagnosis of human constituents. 06. Available from: 1. Agrawal PV, Kumar S, Jain L. Fuzzy rule-based medical expert system to identify the disorders of eyes, ENT and liver. International Journal of Advanced Intelligent Paradigm. 1 Dec; 7(3/): Vol 9 () November 16 Indian Journal of Science and Technology