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- W4382995761 abstract "In the modern world, people's health is one of their top priorities. As a result of inadequate nutrition, many people in the modern world are afflicted with a variety of ailments. Diabetes is one of the most multifaceted and life-threatening illnesses present in the world today. It is difficult to accurately predict diabetes and treat it in a timely manner. Deep ensemble modeling for early diabetes prediction determines whether a person is diabetic or not, and if diabetic then suggests appropriate meals to control it. In this study, the author employed an ensemble machine learning model that was trained to predict whether a person has diabetes or not. If they do, a three-meal diet is advised. Body mass index and calorie needs are determined by a person's weight status, including whether they are overweight, underweight, or healthy. The system will provide a list of food items along with their calorific value for the morning, afternoon, and evening. The proposed system's experimental findings demonstrate its efficacy in anticipating diabetes and recommending foods to manage it." @default.
- W4382995761 created "2023-07-04" @default.
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- W4382995761 date "2023-01-01" @default.
- W4382995761 modified "2023-09-25" @default.
- W4382995761 title "Early Diabetes Prediction Using Deep Ensemble Model and Diet Planning" @default.
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- W4382995761 doi "https://doi.org/10.1007/978-981-99-1373-2_8" @default.
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