Solitary Identification and Analysis of Considerate Nutritional Consumption and Identifying Malnutrition
Keywords:
Computer vision, Haar Cascade classifier, linear regressions, data analytics, photo-ageingAbstract
The food supply chain is in danger from various factors, including climate threats, such as heat waves, floods, droughts, and other extreme occurrences such as intensive livestock farming, demand for animal protein, practices for antimicrobial resistance and various other factors leading to serious food shortages and an increase in food prices. Malnutrition is a major issue, with an estimated 663 million individuals affected. Poor nutrition is a serious problem that affects a vast number of people globally. Recurring infections, which make the issue worse, add to this dilemma. The fact that 22% of children under the age of five are considered “stunted,” meaning they are much shorter than the usual child of their age, is one of the most alarming effects of this problem. Additionally, a sizeable portion of people—over 697 million—who account for 9% of the global population—experience extreme food insecurity. The majority of fatalities occur in mostly high and medium poverty nations. One of major factors that helps us identify the quality of nutritional consumption is the body's main defense against external aggressions is its skin, which is affected by chronological ageing and photo-ageing. Poor eating habits and nutritional imbalances are major contributors to undernutrition and overnutrition among individuals, and also obesity, so it is important to build a machine learning model to regulate the flow of nutritional consumption and depict malnutrition among individuals. This model can be used to anticipate malnutrition on an individual basis, making it a more precise and effective method of recognizing the need to avoid incorrect food intake.
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