Heart attack is among a few of the deadly diseases that cause the death of thousands of people each year globally. It is possible to minimize morbidity and mortality by early screening for those who are at high risk of getting acute myocardial infarction (AMI), known as a heart attack. Android software was implemented to anticipate the risk of getting a heart attack to walk of sudden death. We conducted a survey and collected clinical data from 835 patients that have been analyzed and correlated with 14 risk factors. To predict the heart attack, we used the neural network technology to learn from the clinical data and make predictions. We chose Nesterov-accelerated adaptive moment estimation (Nadam) as an optimizer and categorical cross-entropy as loss function as it fit the best for our neural network model for the best prediction performance. We were able to train our model to predict AMI with 91% accurately. Then, we evaluated our model performance by computing sensitivity (i.e., 81%), specificity (i.e., 98%), precision (i.e., 96%), and F1-score is (i.e., 88%). This trained model was used to implement android software. A user has to answer 14 questions, and based on these answers, the software will predict if the user has a chance to get AMI. This software is free to use, and anyone can use it. The main goal of our research is to implement a simple system to track AMI on a daily basis to lead a healthy life and to avoid sudden deaths.
- Artificial neural network (ANN)
- Machine learning
- Acute myocardial infarction
- Artificial intelligence
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We would like to acknowledge Rural Health Progress Trust (RHPT), Murud, Latur, India, AFC Fortis Escorts Heart Institute, Khulna, Bangladesh and Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh for giving us their valuable information and clinical support in collecting the data.
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Raihan, M., Nazmos Sakib, M., Nizam Uddin, S., Arin Islam Omio, M., Mondal, S., More, A. (2021). Smartphone-Based Heart Attack Prediction Using Artificial Neural Network. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-0586-4_22
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