Produção Nacional
Prediction of Patients’ Incurable Diseases Utilizing Deep Learning Approach
2023; Springer International Publishing; Linguagem: Inglês
10.1007/978-981-99-3315-0_4
ISSN2367-3370
AutoresS. Praveenkumar, Vivek Veeraiah, Sabyasachi Pramanik, Shaik Mahaboob Basha, Aloísio Vieira Lira Neto, Victor Hugo C. de Albuquerque, Ankur Gupta,
Tópico(s)Machine Learning in Healthcare
ResumoAn accurate inquiry may aid timely infection diagnosis, patients’ communal security, and community amenities in today’s world, where data is quickly developing in fields such as bioscience and health protection. In the field of medicine, prediction is an important but sometimes overlooked component. In this paper, the authors construct deep learning and ML approaches in the estimation of chronic illnesses in patients. Conduct several tests using the revised model of prediction based on the standard dataset at your disposal. The purpose of this study is to predict the occurrence of chronic diseases in patients by employing the machine learning technique, KNN, decision tree, and DL employing (ReLU or rectified linear activation function and sigmoid activation function), with Adam serving as an optimizer. When compared with a number of standard algorithms, the suggested system’s accuracy improves significantly. When compared to different approaches, the DL approaches will produce a superior accuracy, which is around 97.9%. Approaches like this are used in making predictions about chronic illnesses including diabetes, heart disease, and breast cancer.
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