Stroke Prediction Using Machine Learning Algorithms

K, Harshitha; P, Harshitha; Gupta, Gunjan; Vaishak, P.; Prajna, K.

doi:10.21276/ijirem.2021.8.4.2

Cited by 10 publications

(1 citation statement)

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“…al. [13]outlines a strategy for enhancing the services offered in the healthcare industry for patient monitoring and wellbeing. This system uses a number of sensors to sense the patient's surroundings and situations, which are then processed to determine the patient's conditions of existence.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Application of IoT Framework for Prediction of Heart Disease using Machine Learning

Swain,

Behera,

Swain

et al. 2023

IJRITCC

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Prognosis of illnesses is a difficult problem these days throughout the globe. Elder people of twenty years and over are taken into consideration to be laid low with this sickness now a days. For example, human beings having HbA1c level more than 6.5% are diagnosed as infected with diabetic diseases. This paper uses IoT to evaluate threat factors which have been similar to heart diseases which are not treated properly. Diagnosis, prevention of heart disease may be done by use of machine learning (ML). There has been an extensive disconnect among Machine Learning architects, health care researchers, patients and physicians in their technology. This paper intends to perform an in-intensity evaluation on Machine Learning to make us of new advance technologies. Latest advances within the development of IoT implanted devices and other medicine delivery gadgets, disease diagnostic methods and other medical research have considerably helped human beings diagnosed heart diseases. New soft computing models can be helpful for remedy of various heart diseases. The Food and Drug Administration (FDA) employs several particularly creative thoughts to get their capsules to the client. Artificial Neural Community offers a first-rate chance to deal with heart diseases with advance IoT and cloud applications.

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Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Application of IoT Framework for Prediction of Heart Disease using Machine Learning

Swain,

Behera,

Swain

et al. 2023

IJRITCC

View full text Add to dashboard Cite

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Predictive modelling and identification of key risk factors for stroke using machine learning

Hassan,

Gulzar Ahmad,

Ullah Munir

et al. 2024

Sci Rep

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Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. However, addressing hidden risk factors and achieving accurate prediction become particularly challenging in the presence of imbalanced and missing data. This study encompasses three imputation techniques to deal with missing data. To tackle data imbalance, it employs the synthetic minority oversampling technique (SMOTE). The study initiates with a baseline model and subsequently employs an extensive range of advanced models. This study thoroughly evaluates the performance of these models by employing k-fold cross-validation on various imbalanced and balanced datasets. The findings reveal that age, body mass index (BMI), average glucose level, heart disease, hypertension, and marital status are the most influential features in predicting strokes. Furthermore, a Dense Stacking Ensemble (DSE) model is built upon previous advanced models after fine-tuning, with the best-performing model as a meta-classifier. The DSE model demonstrated over 96% accuracy across diverse datasets, with an AUC score of 83.94% on imbalanced imputed dataset and 98.92% on balanced one. This research underscores the remarkable performance of the DSE model, compared to the previous research on the same dataset. It highlights the model's potential for early stroke detection to improve patient outcomes.

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