[HTML][HTML] AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes
V Jackins, S Vimal, M Kaliappan, MY Lee - The Journal of …, 2021 - Springer
Healthcare practices include collecting all kinds of patient data which would help the doctor
correctly diagnose the health condition of the patient. These data could be simple symptoms …
correctly diagnose the health condition of the patient. These data could be simple symptoms …
Application of machine learning in disease prediction
PS Kohli, S Arora - 2018 4th International conference on …, 2018 - ieeexplore.ieee.org
The application of machine learning in the field of medical diagnosis is increasing gradually.
This can be contributed primarily to the improvement in the classification and recognition …
This can be contributed primarily to the improvement in the classification and recognition …
Novel binary logistic regression model based on feature transformation of XGBoost for type 2 Diabetes Mellitus prediction in healthcare systems
Y Wu, Q Zhang, Y Hu, K Sun-Woo, X Zhang… - Future Generation …, 2022 - Elsevier
The rapidly increasing incidence of Diabetes Mellitus (DM) has shown that DM is a serious
disease that endangered human life in all parts of the world. The late stage of Type-II DM …
disease that endangered human life in all parts of the world. The late stage of Type-II DM …
Prediction and diagnosis of diabetes mellitus—A machine learning approach
VV Vijayan, C Anjali - 2015 IEEE Recent Advances in …, 2015 - ieeexplore.ieee.org
Diabetes is a disease caused due of the expanded level of sugar fixation in the blood.
Various computerized information systems were outlined utilizing diverse classifiers for …
Various computerized information systems were outlined utilizing diverse classifiers for …
[HTML][HTML] A systematic review of deep learning methods applied to ocular images
OJ Perdomo Charry, FA González - Ciencia e Ingenieria …, 2020 - scielo.org.co
Artificial intelligence is having an important effect on different areas of medicine, and
ophthalmology is not the exception. In particular, deep learning methods have been applied …
ophthalmology is not the exception. In particular, deep learning methods have been applied …
Timely prediction of diabetes by means of machine learning practices
RP Tripathi, M Sharma, AK Gupta, D Pandey… - Augmented Human …, 2023 - Springer
The quality and quantity of medical data produced by digital devices have improved
significantly in recent decades. This has led to cheap and easy data generation. There has …
significantly in recent decades. This has led to cheap and easy data generation. There has …
[HTML][HTML] DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks
R Priyadarshini, RK Barik, H Dubey - Computation, 2018 - mdpi.com
The use of wearable and Internet-of-Things (IoT) for smart and affordable healthcare is
trending. In traditional setups, the cloud backend receives the healthcare data and performs …
trending. In traditional setups, the cloud backend receives the healthcare data and performs …
Important feature selection & accuracy comparisons of different machine learning models for early diabetes detection
SY Rubaiat, MM Rahman… - … Conference on Innovation …, 2018 - ieeexplore.ieee.org
More than 400 million people in the world have diabetes. High-risk factors of diabetic
individuals vary dramatically, and many patients suffer complications and avoidable harm …
individuals vary dramatically, and many patients suffer complications and avoidable harm …
Combining clinical symptoms and patient features for malaria diagnosis: machine learning approach
Presumptive treatment and self-medication for malaria have been used in limited-resource
countries. However, these approaches have been considered unreliable due to the …
countries. However, these approaches have been considered unreliable due to the …
An affective learning-based system for diagnosis and personalized management of diabetes mellitus
Diabetes Mellitus is a major health problem with high global morbidity and mortality rates.
While, conventional diagnosis methods are based on monitoring blood glucose levels …
While, conventional diagnosis methods are based on monitoring blood glucose levels …