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Top1. Introduction
"Eat your food as your medicines. Otherwise, you have to eat medicines as your food." is the best-awarded word in London(Bowie, 2018). The primary sources of the maximum health-related problem are our diet, nutrition, and daily lifestyle. According to IDHS, India is one of the highest-ranking countries globally for the number of children suffering from malnutrition(Khan & Raza, 2016). According to the global Hunger Index 2017(GHI 17) Report by IFPRI, India ranked 100th out of 119 developing countries with a severe hunger situation. Amongst South Asian nations, it ranks third behind only Afghanistan and Pakistan, with a GHI score of 29.0(Nigam, 2018).
As we know, developing countries like India have the most significant number of adults under the grip of many health-related issues. Reasons are well-known facts that are the root causes: the mismanaged lifestyle, incredibly unhealthy diet, and physical inactivity play a key role. The absence of a diet plan as per the energy criterion is one of the significant factors to handle intelligently. We know that the rapid change in food habits and lifestyle of southeast Asians are generally high rates of cardiovascular diseases, diabetes, cancers, and Metabolic disorders diseases.
Nowadays, for a Metabolic disorders patient's diet management with proper nutrition is very important, but in our busy lifestyle, we sometimes cannot decide which food, what amount has to take to maintain adequate nutrition. However, each person with a different lifestyle, different food habits creates a selective diet plan(Hu & Tuomilehto, 2007). Since Soft Computing Fuzzy tools are so close to the human way of thinking, Soft Computing tools-based decision making is effective for developing such kind of system that helps us to provide which food and how much it is better according to our personal information, physical activity, environment, food habit, etc.
In the medical literature, it has been observed that medical diagnosis is governed by symptoms, physical observations, family history, frequency of patients of a particular type of disease lifestyle, food habits, and physician's intuition. The food habit of patients creates a lot of health-related issues, and overall improper nutritional inputs are the root cause of the metabolic disorder. The frequency of metabolic disorder issues is highly noticed among rural and urban areas due to unawareness of the nutritional values and their respective consumption according to their daily routine life and work culture.
In the present work, an effort has been made to develop a diet recommendation intelligent decision-making system based on the physical conditions of the Patient, environmental situations, daily lifestyle, food habits, and daily nutritional requirements with proper ratios of the macros (Carbohydrate., Protein, Fat, and fiber) according to the Patient's information.
This article deals with designing and developing of diet recommendation intelligent decision-making system for patients suffering from a metabolic disorder. This system has been tested on real-life data, and its outputs have been discussed with a prominent dietitian of Prayagraj City, India, and her satisfaction with the outputs confirms the validity of the model for public use. This system will work as a referral system between the metabolic disorders patients and the dietitians to properly recommend appropriate decisions as per the Patient's interest.
The entire article has been classified into seven sections. In Section 2, we have discussed a brief overview of the Fuzzy decision-making model, including its application in medical diagnosis problems and our motivation, ideas for the proposed work. In section 3, we describe valuable and essential results that are used to design the proposed system. In Section 4, the proposed diet recommendation system is described. Section 5 is based on the proposed information system's validity with respect to a number of cases. In Section 6, the Degree of match algorithm is applied to validate the output concerning nutritionist suggestions. A sensitivity analysis is also carried out in this section on the impact of small changes in the macros. Section 7 gives a proper conclusion of the current work.