Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis
Objective Cardiometabolic risk prediction algorithms are common in clinical practice. Young
people with psychosis are at high risk for develo** cardiometabolic disorders. We aimed …
people with psychosis are at high risk for develo** cardiometabolic disorders. We aimed …
Machine learning based unified framework for diabetes prediction
Machine learning gained a significant position in healthcare services (HCS) due to its ability
to improve the disease prediction in HCS. Machine learning techniques and artificial …
to improve the disease prediction in HCS. Machine learning techniques and artificial …
A survey on data mining techniques used in medicine
SM Birjandi, SH Khasteh - Journal of diabetes & metabolic disorders, 2021 - Springer
Data mining is the process of analyzing a massive amount of data to identify meaningful
patterns and detect relations, which can lead to future trend prediction and appropriate …
patterns and detect relations, which can lead to future trend prediction and appropriate …
A proposed model for lifestyle disease prediction using support vector machine
Diseases that are associated with the way a person or group of people live are known as
lifestyle diseases. Healthcare industry collects enormous disease-related data that is …
lifestyle diseases. Healthcare industry collects enormous disease-related data that is …
[HTML][HTML] Cocreation of massive open online courses to improve digital health literacy in diabetes: pilot mixed methods study
Background: Self-management education is a fundamental aspect in the health care of
people with diabetes to develop the necessary skills for the improvement of health …
people with diabetes to develop the necessary skills for the improvement of health …
Obesity level prediction based on data mining techniques
A Alqahtani, F Albuainin, R Alrayes… - … Journal of Computer …, 2021 - koreascience.kr
Obesity affects individuals of all gender and ages worldwide; consequently, several studies
have performed great works to define factors causing it. This study develops an effective …
have performed great works to define factors causing it. This study develops an effective …
Analyzing student's learning interests in the implementation of blended learning using data mining
Blended Learning combines teaching and learning activities in the classroom and online
teaching. In its implementation, this learning method requires a lot of data. One of them is the …
teaching. In its implementation, this learning method requires a lot of data. One of them is the …
Obesity related disease prediction from healthcare communities using machine learning
NC Pereira, J D'souza, P Rana… - 2019 10th International …, 2019 - ieeexplore.ieee.org
In addition to the rapid growth of Machine Learning in biomedical and healthcare
communities, the accurate analysis of medical data benefits early disease detection, patient …
communities, the accurate analysis of medical data benefits early disease detection, patient …
[HTML][HTML] Patient-generated data analytics of health behaviors of people living with type 2 diabetes: sco** review
Background Complications due to type 2 diabetes (T2D) can be mitigated through proper
self-management that can positively change health behaviors. Technological tools are …
self-management that can positively change health behaviors. Technological tools are …
Parkinson's disease classification using Machine Learning techniques
Parkinson disease is one of the serious illnesses that causes mortality at advanced stage. It
is estimated that 9.6 million people are affected throughout the world. These statistics dictate …
is estimated that 9.6 million people are affected throughout the world. These statistics dictate …