Prediction of causes and effects of obesity in India by supervise learning approaches

Mahapatra, Madhumita; Singh, Krishna Kumar

doi:10.1016/j.obmed.2022.100436

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…This highlights the importance of being able to predict obesity in individuals. Also, in ref [24] they analyzed obesity in India using several algorithms such as Xero, EM, Apriori, and Best-First. They then evaluated better-known algorithms such as KNN, Linear Regression, and AdaBoost to predict and/or forecast obesity and gain new insights into the prediction of obesity in people.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Obesity in Nutritional Patients using Decision Tree Modeling

Iparraguirre-Villanueva,

Mirano-Portilla,

Gamarra-Mendoza

et al. 2024

IJACSA

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Obesity has become a widespread problem that affects not only physical well-being but also mental health. To address this problem and provide solutions, Machine Learning (ML) technology tools are being applied. Studies are currently being developed to improve the prediction of obesity. This study aimed to predict obesity levels in nutritional patients by analyzing their physical and dietary habits using the Decision Tree (DT) model. For the development of this work, we chose to use the CRISP-DM framework to follow the development in an organized way, thus achieving a better understanding of the data and describing, evaluating, and analyzing the results. The results of this work yielded metrics with significant values for predicting obesity: so much so that the accuracy rate was 92.89%, the sensitivity rate was 94% and the F1 score was 93%. Likewise, accuracy metrics above 88% were obtained for each level of obesity, demonstrating the effectiveness of the DT model in predicting this type of task. Finally, the results demonstrate that the DT model is effective in predicting obesity, with significant results that motivate further research to continue improving accuracy in this type of task.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting Obesity in Nutritional Patients using Decision Tree Modeling

Iparraguirre-Villanueva,

Mirano-Portilla,

Gamarra-Mendoza

et al. 2024

IJACSA

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“…Additionally, the model was rebuilt and achieved an accuracy rate of 84.59% after integrating the initial learning emotion database with all of the student image data. The outcome demonstrates that the learning emotion recognition model can process the unlearned image through transfer learning to attain excellent recognition accuracy (Singh, 2023;Mahapatra and Singh, 2022).…”

Section: Introductionmentioning

confidence: 96%

A Study on Emotion Analysis and Music Recommendation Using Transfer Learning

Singh,

Dembla

2023

Journal of Computer Science

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As more and more people access and consume music through streaming platforms and digital services, music recommendation has grown in importance within the music industry. Given the abundance of music at our disposal, music recommendation algorithms are essential for guiding users toward new music and for creating individualized listening experiences. People frequently seek out music that fits their current emotional state or desired emotional state, which means that emotions can have a big impact on music recommendations. Emotions can be taken into account by music recommendation algorithms when deciding which songs or playlists to recommend to listeners. Face expressions are frequently used to gauge a person's mood. By using a webcam or any other external device, recognizable facial traits can now be extracted as inputs thanks to modern technology. Transfer learning is a method that is increasingly in demand for enhancing emotion recognition and music recommendation systems in the modern world. Transfer learning has evolved into a potent method for utilizing prior knowledge to enhance model performance and lessen the requirement for massive volumes of labeled data as a result of the data explosion and the availability of big pre-trained models. Hence, the objective of this study is to understand how transfer learning impacts the accuracy of detecting emotions from facial expressions and how the music recommendations can be personalized based on the detected emotions. This study aims at recommending songs by detecting the facial expressions of users using the FER2013 dataset for emotion recognition which is further extended by adding own images to the categories in the dataset from Google. A basic CNN, finetuned pre-trained ResNet50V2, finetuned pre-trained VGG16, and finetuned pre-trained EfficientNet50 B0 are trained on the dataset for emotion detection and compared. The music recommendation system is developed using the Spotify songs dataset extracted using Spotify web API. It uses k-means clustering for grouping tracks based on emotions and getting song recommendations based on the emotion predictions using finetuned ResNet50-V2 model with the highest training accuracy of 77.16% and validation accuracy of 69.04%. The findings reveal that using a transfer learning approach may effectively identify emotions from facial expressions and can have a potential impact on recommending music. It improves duties related to music recommendations and might be a useful method for assisting users in finding new music that fits the intended emotional state.

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