Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991–2020)

R Alizadehsani, M Roshanzamir, S Hussain… - Annals of Operations …, 2021 - Springer
Understanding the data and reaching accurate conclusions are of paramount importance in
the present era of big data. Machine learning and probability theory methods have been …

[HTML][HTML] Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)

S Seoni, V Jahmunah, M Salvi, PD Barua… - Computers in Biology …, 2023 - Elsevier
Uncertainty estimation in healthcare involves quantifying and understanding the inherent
uncertainty or variability associated with medical predictions, diagnoses, and treatment …

An efficient clinical support system for heart disease prediction using TANFIS classifier

J Sekar, P Aruchamy… - Computational …, 2022 - Wiley Online Library
In today's world, the advancement of telediagnostic equipment plays an essential role to
monitor heart disease. The earlier diagnosis of heart disease proliferates the compatibility of …

Optimized ANFIS model using hybrid metaheuristic algorithms for Parkinson's disease prediction in IoT environment

IM El-Hasnony, SI Barakat, RR Mostafa - IEEE Access, 2020 - ieeexplore.ieee.org
Throughout recent years, the progress of telemonitoring and telediagnostics devices for
evaluating and tracking Parkinson's (PD) disease has become increasingly important. The …

A novel CNN-TLSTM approach for dengue disease identification and prevention using IoT-fog cloud architecture

SN Manoharan, KMVM Kumar, N Vadivelan - Neural Processing Letters, 2023 - Springer
One of the mosquito-borne pandemic viral infections is Dengue which is mostly transmitted
to humans by the Aedes agypti or female Aedes albopictis mosquitoes. The dengue disease …

A re-organizing biosurveillance framework based on fog and mobile edge computing

M Al-Zinati, R Alrashdan, B Al-Duwairi… - Multimedia Tools and …, 2021 - Springer
Biological threats are becoming a serious security issue for many countries across the world.
Effective biosurveillance systems can primarily support appropriate responses to biological …

[HTML][HTML] From Data to Diagnosis: Machine Learning Revolutionizes Epidemiological Predictions

AA Abdul Rahman, G Rajasekaran, R Ramalingam… - Information, 2024 - mdpi.com
The outbreak of epidemiological diseases creates a major impact on humanity as well as on
the world's economy. The consequence of such infectious diseases affects the survival of …

Identification of Vector Borne Disease Spread Using Big Data Analysis

DR Ingle, SR Waghmare, V Patil… - … Conference on Smart …, 2022 - ieeexplore.ieee.org
Diseases transmitted by vectors are becoming an increasingly serious problem in India. The
prevention and control of these vector-borne diseases continues to be a struggle for the …

2-(dec-2-enyl)-3-methyl quinolin-4-ol-C20H27NO and 7-amino-N-methyl phenazine-1-carboxamide—C14 H13 N4O2: potent bio-active compounds against dengue …

B Lalithambika, V Chandrapragasam, J Mathew… - International Journal of …, 2023 - Springer
Vector control plays a critical role in achieving reduction in the spread of dengue fever and
dengue haemorrhagic fever among humans. As there are no proper medications for the …

[PDF][PDF] DEVELOPMENT OF PREDICTION MODEL USING MACHINE LEARNING FOR VECTOR BORNE DISEASES IN INDIA

R Kapoor, S Ahuja, V Kadyan - 2021 - researchgate.net
DEVELOPMENT OF PREDICTION MODEL USING MACHINE LEARNING FOR VECTOR
BORNE DISEASES IN INDIA Page 1 DEVELOPMENT OF PREDICTION MODEL USING …