Intelligent health risk prediction systems using  machine learning: a review

Shinde, Santosh A.; Rajeswari, P.

doi:10.14419/ijet.v7i3.12654

Cited by 41 publications

(9 citation statements)

References 30 publications

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“…A decision tree classifier is used to determine the likelihood of the illness. As big data utilization grows in the healthcare and biomedical industries, accurate analysis of medical data helps with early sickness detection and patient treatment [13] so that it can perform better in the future using the learned experience. Machine learning methods application on electronic health record dataset could provide valuable information and predication of health risks.…”

Section: Literature Reviewmentioning

confidence: 99%

Use of machine learning techniques for effective prediction of heart disease

Kotia¹,

Rastogi²,

Bhongade³

2023

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One of the most important problems now affecting the globe is heart disease. A significant problem in the field of clinical knowledge analysis might be disorder prediction. Many medical conditions can be identified, detected and predicted using machine learning. This study uses machine learning methods and Python programming to study heart disease prediction. Heart disease has become a prevalent and fatal condition in the last few years due to the suppression of fat. Excessive pressure in the human body causes this disease to develop. Using multiple features from the dataset, researchers can predict heart disease. To assess patient performance, a dataset consisting of 12 parameters as well as 70000 unique data values was used. The main goal of this study is to increase the accuracy of heart disease detection by using algorithms where the target output determines whether the subject has heart disease. This study provides the base for future heart disease prediction by using the machine learning method.

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Section: Literature Reviewmentioning

confidence: 99%

Use of machine learning techniques for effective prediction of heart disease

Kotia¹,

Rastogi²,

Bhongade³

2023

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“…Machine learning (ML) techniques are common in these situations because they provide better accuracy with big quantities of sensory data than other statistical analyses [ 11 ]. In particular, ML has been used to accurately recognize activities, detect health risk factors and specific health conditions such as frailty or dependence [ 12 – 14 ].…”

Section: Introductionmentioning

confidence: 99%

Systematic design of health monitoring systems centered on older adults and ADLs

Garcia-Moreno,

Bermudez-Edo,

Pérez-Mármol

et al. 2024

BMC Med Inform Decis Mak

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Background Older adults face unique health challenges as they age, including physical and mental health issues and mood disorders. Negative emotions and social isolation significantly impact mental and physical health. To support older adults and address these challenges, healthcare professionals can use Information and Communication Technologies (ICTs) such as health monitoring systems with multiple sensors. These systems include digital biomarkers and data analytics that can streamline the diagnosis process and help older adults to maintain their independence and quality of life. Method A design research methodology is followed to define a conceptual model as the main artifact and basis for the systematic design of successful systems centered on older adults monitoring within the health domain. Results The results include a conceptual model focused on older adults' Activities of Daily Living (ADLs) and Health Status, considering various health dimensions, including social, emotional, physical, and cognitive dimensions. We also provide a detailed instantiation of the model in real use cases to validate the usefulness and feasibility of the proposal. In particular, the model has been used to develop two health systems intended to measure the degree of the elders' frailty and dependence with biomarkers and machine learning. Conclusions The defined conceptual model can be the basis to develop health monitoring systems with multiple sensors and intelligence based on data analytics. This model offers a holistic approach to caring for and supporting older adults as they age, considering ADLs and various health dimensions. We have performed an experimental and qualitative validation of the proposal in the field of study. The conceptual model has been instantiated in two specific case uses, showing the provided abstraction level and the feasibility of the proposal to build reusable, extensible and adaptable health systems. The proposal can evolve by exploiting other scenarios and contexts.

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“…ML algorithms, such as artificial neural networks (ANN) can deliver significantly better solutions than known methods to cycle predictions [17]. As described by [30], machine learning procedures enable the development of intelligence into a machine so that it can achieve better in the future using the learned experience. Moreover, machine learning appliances carry about smart change in the health industry, which includes pattern detection [1], predictions system [20,28], image recognition [24].…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Menstrual Cycle Length: A Time Series Forecasting Approach

Rego

2023

Preprint

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A proper forecast of the menstrual cycle is meaningful for women's health, as it allows individuals to take preventive actions to minimize cycle-associated discomforts. In addition, precise prediction can be useful for planning important events in a woman's life, such as family planning. In this work, we explored the use of machine learning techniques to predict regular and irregular menstrual cycles. We implemented some time series forecasting algorithm approaches, such as AutoRegressive Integrated Moving Average, Huber Regression, Lasso Regression, Orthogonal Matching Pursuit, and Long Short-Term Memory Network. Moreover, we generated synthetic data to achieve our purposes. The results showed that it is possible to accurately predict the onset and duration of menstrual cycles using machine learning techniques.

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Intelligent health risk prediction systems using machine learning: a review

Cited by 41 publications

References 30 publications

Use of machine learning techniques for effective prediction of heart disease

Use of machine learning techniques for effective prediction of heart disease

Systematic design of health monitoring systems centered on older adults and ADLs

Predictive Modeling of Menstrual Cycle Length: A Time Series Forecasting Approach

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