Background and Objective The impact of diet on COVID-19 patients has been a global concern since the pandemic began. Choosing different types of food affects peoples’ mental and physical health and, with persistent consumption of certain types of food and frequent eating, there may be an increased likelihood of death. In this paper, a regression system is employed to evaluate the prediction of death status based on food categories. Methods A Healthy Artificial Nutrition Analysis (HANA) model is proposed. The proposed model is used to generate a food recommendation system and track individual habits during the COVID-19 pandemic to ensure healthy foods are recommended. To collect information about the different types of foods that most of the world's population eat, the COVID-19 Healthy Diet Dataset was used. This dataset includes different types of foods from 170 countries around the world as well as obesity, undernutrition, death, and COVID-19 data as percentages of the total population. The dataset was used to predict the status of death using different machine learning regression models, i.e., linear regression (ridge regression, simple linear regularization, and elastic net regression), and AdaBoost models. Results The death status was predicted with high accuracy, and the food categories related to death were identified with promising accuracy. The Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R 2 metrics and 20-fold cross-validation were used to evaluate the accuracy of the prediction models for the COVID-19 Healthy Diet Dataset. The evaluations demonstrated that elastic net regression was the most efficient prediction model. Based on an in-depth analysis of recent nutrition recommendations by WHO, we confirm the same advice already introduced in the WHO report 1 . Overall, the outcomes also indicate that the remedying effects of COVID-19 patients are most important to people which eat more vegetal products, oilcrops grains, beverages, and cereals - excluding beer. Moreover, people consuming more animal products, animal fats, meat, milk - including butter, sugar and sweetened foods, sugar crops, were associated with a higher number of deaths and fewer patient recoveries. The outcome of sugar consumption was important and the rates of death and recovery were influenced by obesity. Conclusions Based on evaluation metrics, the proposed HANA model may outperform other algorithms used to predict death status. The results of this study may direct patients to eat particular types of food to reduce the possibility of becoming infected with the COVID-19 virus.
Early detection of cancer cases is one of the most important things that help complete treatment and disappearance of this disease from the human body. Breast cancer is the most widespread invasive cancer in women, and after lung cancer, it is the second leading cause of cancer death in women. The first symptoms of breast cancer usually appear as an area of thickened tissue in the breast or a lump in the breast or an armpit. Consequently, many features can be found to indicate the existence of cancer or not. This chapter employs the coral reefs optimization (CRO) algorithm for feature selection; the CRO has shown to be very effective with various classification approaches. In this chapter, we used five standard classifiers: Logistic Regression (LR), K-nearest neighbor (KNN), Support Vector Machine with Radial Basis Function (SVM-RBF), Random Forest (RF), Decision Tree (DT). All These classifiers are presented with and without feature selection using the CRO algorithm. The results indicated that using the feature selection based on CRO achieved better results before the feature selection. The most common dataset called Breast Diagnostic Cancer Wisconsin (BDCW) is utilized to select the most significant and classify the cancer cases with a tested accuracy of 100%, 99.1%, 100%, 100%, and 100% using LR, KNN, SVM-RBF, RF, and DT respectively.
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