Heart Disease Predictions Using Numerous Classification Techniques and Dynamic LSTM Model

Mishra, Aishwarya; Singh, Dayashankar

doi:10.1109/icces45898.2019.9002054

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…F1-score Measure is the mean of recall and precision using the formula given in equation (19). R 2 score represents the proportion of variance of output O. it provides the indication goodness of fit and measures how well the unseen samples are likely to be predicted by the model using equ (20) and (21).…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

An Efficient Disease Prediction System with Electronic Health Record Using Feature Selection and Convolutional Recurrent Neural Network

Sandhiya

2023

Preprint

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Rapid advancements in communication technologies and expert systems generate massive amounts of medical data. Using an electronic health record (EHR) and a disease prediction system, we can predict high-risk patients at an early stage. The use of EHR for early prediction aids in the improvement of health care quality through personalized medicine. To analyze the massive amount of disease-related EHR data, various deep learning methods for disease prediction are proposed. Deep learning is a machine learning technique that has evolved. Even with more deep learning techniques in place, no healthcare system has achieved a higher level of disease prediction accuracy. In this paper, we propose a disease prediction system for serious, lethal diseases such as heart, diabetes, and cancer diseases. The proposed system is made up of a feature selection algorithm (GBCOA) and a Convolutional-Recurrent Neural Network (C-RNN). This proposed model was tested against existing methods and found to have higher prediction accuracy with less computation time.

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Section: Performance Evaluation Metricsmentioning

confidence: 99%