Survival prediction using artificial neural networks in patients with uterine cervical cancer treated by radiation therapy alone

2002 ◽  
Vol 7 (5) ◽  
pp. 0294-0300 ◽  
Author(s):  
T. Ochi ◽  
K. Murase ◽  
T. Fujii ◽  
M. Kawamura ◽  
J. Ikezoe
Keyword(s):  
Neural Networks ◽  
Cervical Cancer ◽  
Radiation Therapy ◽  
Artificial Neural Networks ◽  
Uterine Cervical Cancer ◽  
Survival Prediction ◽  
Artificial Neural

Artificial Neural Networks Applied to Survival Prediction in Breast Cancer

Oncology ◽  
1999 ◽  
Vol 57 (4) ◽  
pp. 281-286 ◽  
Author(s):  
M. Lundin ◽  
J. Lundin ◽  
H.B. Burke ◽  
S. Toikkanen ◽  
L. Pylkkänen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Survival Prediction ◽  
Artificial Neural

scholarly journals Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Tatjana Gligorijević ◽  
Zoran Ševarac ◽  
Branislav Milovanović ◽  
Vlado Đajić ◽  
Marija Zdravković ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Baroreflex Sensitivity ◽  
Training Dataset ◽  
Survival Prediction ◽  
Classification Problems ◽  
Risk Of Death ◽  
Increased Risk ◽  
Artificial Neural

Artificial neural networks (ANNs) are machine learning technique, inspired by the principles found in biological neurons. This technique has been used for prediction and classification problems in many areas of medical signal processing. The aim of this paper was to identify individuals with high risk of death after acute myocardial infarction using ANN. A training dataset for ANN was 1705 consecutive patients who underwent 24-hour ECG monitoring, short ECG analysis, noninvasive beat-to-beat heart-rate variability, and baroreflex sensitivity that were followed for 3 years. The proposed neural network classifier showed good performance for survival prediction: 88% accuracy, 81% sensitivity, 93% specificity, 0.85 F-measure, and area under the curve value of 0.77. These findings support the theory that patients with high sympathetic activity (reduced baroreflex sensitivity) have an increased risk of mortality independent of other risk factors and that artificial neural networks can indicate the individuals with a higher risk.

scholarly journals A Journal on Cervical Cancer Prediction Using Artificial Neural Networks

2021 ◽  
Vol 12 (2) ◽  
pp. 1085-1091
Author(s):  
K.Deepa , Et. al.
Keyword(s):  
Neural Networks ◽  
Cervical Cancer ◽  
Artificial Neural Networks ◽  
Death Rate ◽  
Developed Countries ◽  
Medical Procedures ◽  
Clinical Imaging ◽  
Cancer Prediction ◽  
Artificial Neural ◽  
Cervical Cells

Artificial neural networks (ANN) assume a significant part in numerous clinical imaging applications. Cervical cancer ranks the 4th dangerous women cancers in less developed countries due to insufficient trained staffs and medical procedures. The location of cervical malignancy cells utilizes ANN for characterizing the typical and unusual cells in the cervix wall of the uterus. Cervical malignancy location is exceptionally difficult on the grounds that this disease happens with no manifestations.  The order between the typical,unusual and malignant cells produces exact outcomes than other manual screening techniques.The ANN utilizes a few models for a simple and precise identification of cervical cells. The main aim of artificial neural networks is to supply right information at a right time. Hence we implement artificial neural techniques with collected data Analysis,to improve the life of an individual and to decrease the death rate of the society respectively.   

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