AI-Based Mental Fatigue Recognition and Responsive Recommendation System

Kumar, Korupalli V Rajesh; Sudhakara, Malla; Keerthi, Gabbireddy; Madhavi, K. Reddy

doi:10.1007/978-981-19-4162-7_29

Cited by 1 publication

(1 citation statement)

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“…Bacterial taxa of varying kinds were distinguished. The improved attributes curve that was generated using random forest was 0.70 [9]. The current study incorporated one of the machine learning methods, and the random forest because of its potential in predicting cardiovascular disease.…”

Section: B Key Findings From Literaturementioning

confidence: 99%

Statistical Analysis with Machine and Neural Learning-Based Model on Cardiovascular Diseases and Stroke Prediction

Jayasree¹,

Usha²

2022

IJRITCC

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Several risk factors, such as hypertension, hyperlipidemia, and an irregular heart rhythm, make an early diagnosis of cardiovascular disease challenging. Reducing cardiac risk calls for precise diagnosis and therapy. Clinical practice in the healthcare business is likely to evolve in tandem as a result of advancements in machine learning. Therefore, scientists and doctors need to acknowledge machine learning's significance. The fundamental purpose of this research is to a reliable analyzing Risk Factors for Cardiovascular Disease method that makes use of machine learning. Classifying well-known cardiovascular datasets But, on the other hand, is a job for state-of-the-art machine learning techniques and neural network algorithms. Several statistical and visualization indicators were used to assess the efficacy of the suggested approaches and to determine the optimal machine-learning and neural-network approach. Using these modeling methods acquired high and accurate accuracy on stroke and heart disease prediction.

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Section: B Key Findings From Literaturementioning

confidence: 99%