Machine Learning Algorithms for Diabetes Prediction

AlSideiri, Abir; Cob, Zaihisma Binti Che; Drus, Sulfeeza Bte Mohd

doi:10.1145/3388218.3388231

Cited by 9 publications

(2 citation statements)

References 15 publications

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“…There has been a meteoric rise in the number of machine learning [18] applications that automate and solve issues across many different industries. This is mostly attributable to developments in processing power, advances in machine learning [19] methods, and the proliferation of accessible data.…”

Section: Machine Learningmentioning

confidence: 99%

A Smartphone Sensor Based Real-Life Human Activity Recognition System Using Deep Nets Method

Zainab,

Srivastava

2024

International Journal of Innovative Research in Computer Science and Technology (IJIRCST)

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The proliferation of sensor devices in recent years has made Human Activity Recognition (HAR) a hotspot for academic interest. Smartphones with built-in sensors make it possible to employ such sensors for activity detection tasks like assisting the elderly with their everyday tasks. A plethora of high-tech sensors, such as global positioning systems (GPS), cameras, accelerometers, microphones, light sensors, and compass, are standard on these smartphones. Activity recognition is a promising field of study that might be used to provide consumers with flexible and efficient services. The purpose of our research is to evaluate a system that makes use of accelerometers, which are acceleration sensors that are built into smartphones. For the purpose of running the model and understanding six distinct human activities through supervised machine learning classification, twenty-six users' accelerometer data is collected while they go about their daily lives, including sitting, standing, lying down, walking, and climbing and descending stairs. Following the merging and aggregation of the sample data, supervised machine learning techniques were employed to generate prediction models from the instances. To get beyond the limitations of the lab, we used the Google Android platform and the Physics Toolbox Sensor Suite to gather this time series data. In this work, we take a look at how Machine Learning and Deep Nets have been used to address issues with human activity identification using smartphone sensors. Furthermore, we proved that data collected with lower frequencies can still serve their intended purpose. Our most basic Deep Nets model yielded a maximum accuracy of 95.71% for the male dataset and 94.62 % for the female dataset.

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Section: Machine Learningmentioning

confidence: 99%

A Smartphone Sensor Based Real-Life Human Activity Recognition System Using Deep Nets Method

Zainab,

Srivastava

2024

International Journal of Innovative Research in Computer Science and Technology (IJIRCST)

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“…Applications for machine learning [42] that resolve problems and automate in numerous sectors have increased at an extraordinary rate. This is mostly due to the growth in the amount of information that is available, significant improvements in machine learning [43] techniques, and advancements in computing power.…”

Section: Machine Learningmentioning

confidence: 99%

Novel Approaches to Detect Phony Profile on Online Social Networks (OSNs) Using Machine Learning

et al. 2023

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Currently, almost everyone spends more time on online social media platforms engaging with and exchanging information with people from all over the world, from children to adults. Our lives are greatly influenced by social media sites like Twitter, Facebook, Instagram, and LinkedIn. The social network is evolving into a well-liked platform for connecting with individuals across the globe. Social media platforms exist as a result of the enormous connectivity and information sharing that the internet has made possible. Social media's rising popularity has had both beneficial and detrimental consequences on society. However, it also has to deal with the issue of bogus profiles. False profiles are often constructed by humans, bots, or cyborgs and are used for phishing, propagating rumors, data breaches, and identity theft. Thus, we are emphasizing in this post the significance of setting up a system that can identify false profiles on social media networks. To illustrate the suggested concept of machine learning-based false news identification, we used the Twitter dataset for phony profile detection. The suggested model involves pre-processing to improve the dataset's quality and minimize its dimensions by modifying its contents and features. To forecast the bogus profiles, the widely used machine learning algorithms are used.

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A Comparative Analysis of Machine Leaming-Based Classifiers for Predicting Diabetes

Aggarwal,

Basha,

Arya

et al. 2023

2023 International Conference on Advanced Computing &Amp; Communication Technologies (ICACCTech)

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Machine Learning Algorithms for Diabetes Prediction

Cited by 9 publications

References 15 publications

A Smartphone Sensor Based Real-Life Human Activity Recognition System Using Deep Nets Method

A Smartphone Sensor Based Real-Life Human Activity Recognition System Using Deep Nets Method

Novel Approaches to Detect Phony Profile on Online Social Networks (OSNs) Using Machine Learning

A Comparative Analysis of Machine Leaming-Based Classifiers for Predicting Diabetes

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