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Methods to Predict the Performance Analysis of Various Machine Learning Algorithms
Published in K Hemachandran, Shubham Tayal, Preetha Mary George, Parveen Singla, Utku Kose, Bayesian Reasoning and Gaussian Processes for Machine Learning Applications, 2022
M. Saritha, M. Lavanya, M. Narendra Reddy
This is the opposite of recall. Specificity is the ratio between the True NegativeTN and the sum of True NegariveTN and Fasle PositiveFP (ML Metrics: Sensitivity vs. Specificity – DZone AI, n.d.). As a result, the denominator (TN + FP) equals the total number of negative occurrences in the collection. It is comparable to recollection, except the emphasis is on unpleasant events. For example, how often healthy people were told they did not have cancer when they did not. It is a kind of test to check how distinct the categories are. Specificity=TrueNegative(TN)TrueNegative(TN)+FalsePositive(FN)
Effect of a mobile application on the precision of the preliminary diagnosis of anxiety
Published in Cogent Engineering, 2020
Walter Junior Mayo Espinoza, Emigdio Antonio Alfaro Paredes
The sensitivity and the specificity of the disease diagnosis are the measures of the validity of a test (Stojanovic et al., 2014, p. 1062). Stojanovic et al. (2014) also explained: For a test to be accurate, both sensitivity and specificity should be high. When measuring sensitivity, we only calculate those people with disease. High sensitive test detects a high percentage of positive cases while missing few. Also, a negative result would suggest the absence of disease according to test with high sensitivity. On the contrary, specificity highlights negative test results. A highly specific test is good for detection of a disease if a person tests positive, likewise it does not falsely diagnose disease when none is present. (p. 1064)
Cost-effectiveness analysis of prognostic-based depression monitoring
Published in IISE Transactions on Healthcare Systems Engineering, 2019
Ying Lin, Shuai Huang, Gregory E. Simon, Shan Liu
The monitoring accuracy of each strategy can be measured by the percentage of severely depressive patients being monitored (sensitivity) and the percentage of healthy to moderately depressive patients not monitored (specificity). The different choice of threshold may result in different levels of sensitivity and specificity. For instance, increasing the threshold leads to an increase in specificity and a decrease in sensitivity. To ensure the best tradeoff between sensitivity and specificity, we find the optimal threshold for each prognostic model using the validation data at the sixth month, which minimizes the distance between monitoring accuracy and perfect monitoring (sensitivity = 1 and specificity = 1); i.e., where sensitivity specificity
A semiparametric method for estimating the progression of cognitive decline in dementia
Published in IISE Transactions on Healthcare Systems Engineering, 2018
Xiaoxia Li, Canan Bilen-Green, Kambiz Farahmand, Linda Langley
Although there is a tendency to perform trajectory studies for different dementia types, such as AD, Lewy body dementia, and Parkinson's dementia, we did not consider the diagnosis of dementia type as a parameter in the current study. The first reason for this is that the diagnostic methods are not sufficiently accurate to classify patients with enough precision to carry out a study based on diagnostic results. In medical diagnoses, sensitivity and specificity measure the ability to identify those with the disease correctly (true positive rate) and those without the disease (true negative rate). A study by Beach et al. (2012) reported that the sensitivity in the diagnosis of AD ranged from 70.9% to 87.3%, and specificity ranged from 44.3% to 70.8% for NACC data. Clark et al. (2011) reached a similar conclusion, stating that 10% to 20% of patients clinically diagnosed with AD did not have AD pathology. The presence of mixed dementia is another factor that makes diagnosis difficult, because of the coexistence of more than one neuropathology. A sample study by Schneider et al. (2007) showed that, among community-dwelling older individuals with dementia, 54% showed pathological evidence of one or more coexisting dementias. All of these factors make it difficult to make rigorous diagnoses. The inclusion of the heterogeneous cognitive decline patients would add variance to the models, but would help to access a more general cognitive decline trajectory that does not need specific diagnoses.