Optimizing Nutrition using Machine Learning Algorithms-a Comparative Analysis

Khan, Asmabee; Deshpande, Satish P.; Tripathy, Amiya Kumar

doi:10.1109/icnte44896.2019.8946091

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…For this purpose, three levels of quality have been defined: High (H) the articles with a score between 8 and 11, Medium (M) between 5.1 and 7.9, and Low (L) less than 5. After the qualification, we can affirm that within the metrics used in this study, the articles [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] are of high impact.…”

Section: Resultsmentioning

confidence: 68%

Systematic Review of Nutritional Recommendation Systems

2021

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In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society. There are many apps created to count calories based on what we eat, or to estimate calorie consumption according to the sport we do, or to recommend recipes, but very few are capable of giving personalized recommendations. This review tries to see what studies exist and what recommendation systems are used for this purpose, over the last 5 years in the main databases. Among the results obtained, it is observed that the existing works focus on the recommendation system (usually collaborative filtering), and not so much on the description of the data or the sample analyzed; the indices used for the calculation of calories or nutrients are not specified. Therefore, it is necessary to work with open data, or well-described data, which allows the experience to be reproduced by third parties, or at least to be comparable. In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society.

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Section: Resultsmentioning

confidence: 68%

Systematic Review of Nutritional Recommendation Systems

2021

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“…Calorie, protein, fat and carbohydrate counts are important in health care and diet management because they can be accurately predicted and analysed (Khan, Deshpande, and Tripathy 2019). Routine health information from the health management information system (HMIS) was analysed in this study to see whether it was being used effectively.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Analysis for Image Classification and Identification of Nutritional Values Using Convolutional Neural Networks in Comparison with Logistic Regression Model

2022

pnr

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The goal is to raise public awareness about nutritional issues by using food images to predict nutritional analysis using a novel image classification technique. Methods and Materials: The proposed research will be conducted at our university, and a total of two groups have been formed. There are two types of neural networks: a convolutional neural network and a logistic regression network. The framework uses 10 samples per group to evaluate accuracy. Gpower of 80% was used to calculate the sample size. Results: Convolutional Neural Network algorithm has predicted the nutritional analysis with the accuracy of (83.84%) which is more compared with the Logistic algorithm (72.3%) in identifying the fruit, Calorie count, amount of protein content, total fat, and subsequently carbohydrates measurement and so on. There is no statistically significant difference with (P = 0.092, >.05) among the classification algorithms. Conclusion: The analysis shows that the Convolutional Neural Network is significantly better for the whole Nutrition Analysis process compared to the Logistic regression.

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“…Beberapa penelitian menujukkan kinerja kedua algoritma tersebut cukup optimal untuk diterapkan dalam penelitian ini dibanding jenis algoritma lain. Seperti penelitian yang menggunakan random forest dan support vector machine untuk klasifikasi makanan dengan hasil akurasi kedua algoritma yang tidak mencapai 90% [7]. Selain itu, metode knapsack juga pernah digunakan dalam penelitian terkait rekomendasi makanan yang mempertimbangkan penyakit kronis, namun hasilnya memiliki kompleksitas waktu yang tidak cukup baik [8].…”

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Rekomendasi Makanan Pasien Hiperlipidiemia Berdasarkan Hasil Klasifikasi Menggunakan Metode Naïve Bayes dan Decision Tree

Laila,

Widiarto,

Wijayanto

et al. 2022

JEPIN

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Makanan merupakan kebutuhan manusia untuk memenuhi nutrisi dalam keberlangsungan hidup. Namun setiap orang perlu memperhatikan makanan yang dikonsumsi karena akan memengaruhi kondisi tubuh. Salah satu zat dalam tubuh manusia yang perlu diperhatikan ketika mengonsumsi makanan adalah lemak. Penelitian terkait konsumsi makanan dengan kadar lemak dalam tubuh sudah banyak dilakukan. Terbukti terdapat jenis makanan yang dapat memengaruhi kesehatan tubuh karena menyebabkan zat lemak yang berlebihan. Penelitian yang dilakukan kali ini adalah klasifikasi data kebutuhan gizi dengan membandingkan algoritma naïve bayes dan decision tree. Hasil klasifikasi antara kedua algoritma tersebut digunakan untuk memberikan rekomendasi makanan yang sesuai untuk dikonsumsi oleh orang yang menderita hiperlipidemia, yakni kondisi di mana kadar lemak dalam tubuh berlebihan. Penelitian dimulai dari pengumpulan data, pra-proses data dengan normalisasi, klasifikasi dengan beberapa model naïve bayes dan algoritma decision tree, menganalisis hasil klasifikasi dengan confusion matrix, dan melakukan implementasi rekomendasi makanan berdasarkan hasil model klasifikasi yang paling optimal. Pada penelitian ini diperoleh hasil bahwa model ComplementNB memiliki akurasi tinggi dibanding model lain namun secara keseluruhan algoritma decision tree lebih stabil dibanding naïve bayes.

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Optimizing Nutrition using Machine Learning Algorithms-a Comparative Analysis

Cited by 4 publications

References 6 publications

Systematic Review of Nutritional Recommendation Systems

Systematic Review of Nutritional Recommendation Systems

Accuracy Analysis for Image Classification and Identification of Nutritional Values Using Convolutional Neural Networks in Comparison with Logistic Regression Model

Rekomendasi Makanan Pasien Hiperlipidiemia Berdasarkan Hasil Klasifikasi Menggunakan Metode Naïve Bayes dan Decision Tree

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