Expert Systems For Human Nutrition And Diet
Developing a nutrition and diet expert system prototype. Expert systems, a type of AI technologies, encode human expertise in specific domains by using If-Then rules, and accordingly advise and provide solutions to different problems. An expert system comprises of five components which are
- User interface
- Working memory
- Knowledge base
- Inference engine
- Explanation system.
A nutrition expert system helps people to evaluate their nutrition status and conditions andaccordingly provides them with nutrition and diet advises. Moreover, it will help people to save their time as well as their money they will not need to visit the doctor to evaluate their nutrition conditions. A nutrition expert system is to ask user questions related to age, weight, height, gender, and exercise time and type, and accordingly advise them. Acquiring knowledge in any field including nutrition is not an easy process. The objective of this developed nutrition and diet expert system is to help individuals to evaluate their nutrition condition and to know the type of food and required time to do exercising each day. The system provides advices about healthy food and the rate of proteins, vitamins, and calcium they must eat. It also advices about sports for different conditions. The gaps of the system are
- The method of calculating waist circumference is unknown.
- Boring style.
- Unclear solutions.
- Not covering everything related to nutrition.
- Not as good as having human expert to hand.
- Cannot be opened without java.
Expert system for nutrition care process of older adults. Dietary knowledge is defined as Nutrition Care Process Ontology, and then used as a base and standardized model for the nutrition care planning. An inference engine is developed on top of the ontology, providing semantic reasoning infrastructure and mechanisms for evaluating the rules defined for assessing short- and long-term elders’ self-feeding behaviours, to identify unhealthy diet and detect the early instauration of malnutrition. Nutrition Monitoring Ontology interprets data by describing the older adults feeding behaviour such as personal information, lifestyle information, social information and physical activities information. Then the rest are based anthropometry, biochemistry, clinical and dietary information which are popularly known as “ABCD approach”.
Anthropometry information is gotten from the measurement of aspect of the body. For example, Height, Weight used to calculate the BMI (Body Mass Index). Biochemistry information is required for individuals with special diet plans that is because of health challenges.
Clinical information includes health problems, medical diagnoses, use of prescribed medication or supplements. This article provides appropriate computational for diet knowledge that is clinically informed and constructs knowledge base on which reasoning and query processes can be executed for dietary assessment of older adults.
The gap highlighted in this article is that its static nature is not able to adapt the menus and recipes for some nutritional profiles. For example, diabetic patients and pregnant women.
Personalized weight management intervention for cardiovascular risk reduction: a viable option for African American women. Obesity is a major cause of cardiovascular disease (CVD) and a major cause of racial/ethnic and gender disparities. Weight management through lifestyle management is very important for reduction of cardiovascular disease health disparities in African American women. Web-based technology was used because it offers innovative and potential benefits to countering obesity, reducing CVD among African American women. To fully understand how AI techniques can be used to personalize Web-based interventions, it is important to first understand their basic construction. The knowledge base contains stored facts or knowledge and fixed statements called rules. A system can work by applying facts or knowledge stored in the knowledge base to solve problems. The inference examines the knowledge and facts in the knowledge base. After examining it, actions will be taken if the users information satisfy the conditions in the rule. The contribution this article made was the customised algorithms that can assess user lifestyle practices.
A service-based system for malnutrition prevention and self-management. Malnutrition is one of the major causes for the occurrence of some diseases most especially in aging population. This article shows design of a distributed system that takes care of homecare management in the context of self-feeding and the prevention of malnutrition through the intake of balanced diet. It is a service-based system that includes services like monitoring of physical activities, diet recommendation for food planning, nutritional reasoning for computing eating habits, and marketplace invocation for automating food shopping to meet dietary requirements. The consequences of malnutrition have a major impact on the aged population’s wellbeing, worsening of chronic conditions and delaying recovery from illnesses. The cost budgeted for malnutrition in Europe is approximately 170 billion euro each year.
Monitoring and detecting the elders’ food intake in home settings looks like intrusion of privacy but in this system, it is done with minimal intrusion, using user-friendly graphical user interfaces and user experience to fit the elderly’s capabilities and ease of use. Malnutrition prevention process is a set of continuous loops of step that start with monitoring then to collecting food intake information with each step relying on the input of the previous step. The standardised models of nutrition care planning process were implemented. The overall system design uses services to enable collection of nutritional related data and providing a related recommendation directly to the older adults in their home as well as to their dietician and informal carers. Food and meal plan generation services receive unhealthy behaviours from the user monitoring system, nutritional deficiencies information and personalized nutritional goals into a type of computational form. They are gotten from the assessment and recommendation services.
This information is then used by the systems diet plan generation services, to generate food intake meals and food plans that can help to overcome identified diet and nutritional deficiencies. The service-based mechanism in the system provides well-defined diet specific marketplace that allows food vendors to register their services and food offerings. The gap is when developing methods to ensure the reliability of food provision, the support of multi-cultural framework of service composition and selection, the sensor-driven monitoring of older adults’ nutritional behaviour may not be accepted. The on-time responses to prevent malnutrition or to treat malnutrition, the ability to adapt to multiple cultural and personal preferences.
An artificial intelligence framework for compensating transgressions and its application to diet management. Patient empowerment is a challenging goal for medical informatics. The use electronic sensors to acquire user data is a basic principle of “quantified self”, that has been adopted to designate a field of study for the monitoring of human activities. The use of STP framework was implemented. There are various limitations concerning the use of STP framework. They are:
- It doesn’t support directly the view over of the constraint satisfaction problem.
- It doesn’t allow to model the system-user interaction.
- No formal distinction between constraint modelling indeterminacy over the past and constraint modelling possibilities over the future.
These limitations were taken care of in the article. A constraint solver aimed at solving constraint of bit vectors and arrays and restricts the distance between two points a lower bound and an upper bound. is called STP. Several methods were implemented which are:
- Computing the minimal network of a diet.
- Compatibility check of a single meal with respect to diet.
- Computing the minimal network is a diet with respect to the meals taken.
- Simulation of several meals with respect to a diet.
- Listing the dietary constraint represented in the minimal network.
- Choosing the best meal with respect to a diet.
- Evaluating a meal with respect to a diet.
The gap present is it does not present general architecture for diet it focuses mainly on reasoning system and doesn’t also discuss the natural language generation module.
Artificial intelligence and dietician
The online artificial dietician is a website with intelligence about diets. It is an expert system that acts just like a real diet expert. It consults in a similar way that a real dietician would. Dieticians expert educated with nutrient value of foods and years of experience. A dietician uses a person’s schedule, body type, height and weight to recommend diet plans to a person. The system does the same by asking all this data from the user and processes it. The system asks about daily schedule of the individual, his weight, height, age etc. The system stores the information given by the user and processes the information. It calculates the nutrient value needed by the user. The system then shows a diet plan to the user and ask the user if the diet plan is ok, else it shows alternate diets to satisfy the user needs. The system is a very responsive website that contains data concerning individual fitness. Data required to develop the system was referred from a gym exercise book.
Diet expert system in a rule-based inference engine
Obesity and other eating disorders are on a major increase in South Africa most especially among female adolescents. Schönfeldt et al (2010) pointed out that insufficient education as regards to eating habits appropriate for individuals and inability to gain the necessary education due to financial limitations. Linear programming is mostly used for solving complex real problems in various fields, including dietetics. Expert systems use knowledge base and inference engine to solve complex problems and requires expert knowledge and it is also applied to health-related problems. In this article, an expert system was created for solving multiple dieting problem, by creating a rule-based inference engine consisting of goal programming- and multi-objective linear programming models. Whitney and Rolfes (2008) use factors like age, gender, height, weight, activity level and some values obtained through calculations, considering exactly these factors when developing a healthy diet for an adolescent. Establishing a solution to the dieting problem specifically with South African adolescent girls by developing an expert system consisting of a linear programming model that represents the rule-based inference engine was the purpose of this research. The expert system layout consists of components like
- A user interface that collects facts from the user for the system.
- A working memory which temporarily holds information entered by the user.
- An inference engine which contains the set of production rules.
- A knowledge base that consist of knowledge gotten from experts in the field of dietetics.
During knowledge definition, a lot of sources are needed to be explored to create and establish a comprehensive knowledge base required for the system. Sources consulted and used in this research include human experts (practicing dieticians and lecturers in dietetics), electronic and internet resources, books, academic study material and publications and journal articles. The knowledge definition resulted in the following functional requirements for the system:
- The system should calculate the daily energy requirement.
- The system should present food lists to the user for food preference acquisition.
- The system should optimize user food preference and total cost by solving the linear programming models in the inference engine.
- The system should provide output to the user.
The inference engine and a part of the knowledge base of the expert system was implemented using goal programming model and a multi-objective linear programming model. The goal programming model was implemented to determine from each exchange group the number of exchanges to be chosen so that the macro-nutrient distribution is as close as possible to the percentages either entered by the user or chosen by the dietician. The expert system was evaluated in terms of two criteria which are the result of changing the weighted priorities for cost and the system was applied to the six available real-world case studies and the results were compared. The expert system technology can be successfully combined with mathematical programming techniques by integrating linear programming models to perform rule-based inference. The work gives some valuable contributions to linear programming and expert system fields.
Preparing diet list suggestion
Proportion of disease is growing due to the sedentary lifestyle and malnutrition. Every nutrition plays an important role for supporting day to day activities of individuals. A balanced diet should be in certain quantity of carbohydrate, protein, fat, vitamins and minerals. Quantity of these nutrients should be calculated by considering taken and consumed energy. Nevertheless, people do not go to dietitian because of their habits and busy schedule. To overcome this problem, website based expert systems that have input parameters such as current diseases, activity level and age should be used.
Most studies which are focused on diet planning for humans are focused on a specific region or a situation. The diet recommendation was offered by using Knapsack Method and Fuzzy Rules that utilizes users’ weight, height, activity level, hypertension, renal function, high cholesterol and preference data.
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