Develop a Model for Assessing the Most Efficient Diseases Diagnosis using Machine Learning

• Autores: Vives, Luis, Basha, N. Khadar, Poonam, Gehlot, Anita, Chole, Vikrant, Pant, Kumud
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/660901
• Enlace del recurso: http://hdl.handle.net/10757/660901
• Nivel de acceso: acceso embargado
• Materia: Algorithm; automatic assistance; classification; clustering; Data Acquisition; Data Management; Data processing; Data protection; data wrangling; Deep learning; Healthcare; imputation; Internet of things; Interpretation; probabilities; regression; Security; statistics; supervised learning

so, machine learning techniques are being developed to improve performance and maintenance prediction. Increasing our knowledge of the relationship between humans and algorithms, Because data is so valuable, improving strategies for intelligently having to manage the now-ubiquitous content infrastructures is a necessary part of the process toward completely autonomous agents. Numerous researchers recently developed numerous computer-aided diagnostic algorithms employing various supervised learning approaches. Early identification of sickness may help to reduce the number of people who die as a result of these illnesses. Using machine learning techniques, this research creates an efficient automated illness diagnostic algorithm. We chose three key disorders in this paper: coronavirus, cardiovascular diseases, and diabetes. The data are inputted into a mobile application in the suggested model, the investigation is then done in a real-time dataset that used a pre-trained model machine learning technique trained within the same dataset then implemented in firebase, and lastly, the illness identification result can be seen in the mobile application. Logistic regression is a method of prediction calculation