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Applications of Machine Learning in Cancer Prediction and Prognosis
Published in Meenu Gupta, Rachna Jain, Arun Solanki, Fadi Al-Turjman, Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective, 2021
Geetika Sharma, Chander Prabha
There are various performance analysis parameters used for analyzing the performance of any classification model like precision, specificity, sensitivity, and area under the curve (AUC). Accuracy means the total number of correct or accurate predictions. This accuracy is predicted from the testing set. Sensitivity is defined as the total number of true positives, whereas specificity is defined as the total number of true negatives. In the end, the model's performance is measured in terms of AUC, which is based on the graph drawn for the trade-offs between specificity and sensitivity, known as the ROC curve (Figure 8.1). For analyzing the performance and to obtain more reliable results, we should have large training and testing datasets and should have good knowledge about all the labels of the testing dataset.
Measurement Systems: Calibration and Response
Published in Patrick F. Dunn, Fundamentals of Sensors for Engineering and Science, 2019
From a calibration experiment, a calibration curve is established. A generic static calibration curve is shown in Figure 6.1. This curve has several characteristics. The static sensitivity refers to the slope of the calibration curve at a particular input value, x1. This is denoted by K, where K = K (x1) = (dy/dx)x=x1. Unless the curve is linear, K will not be a constant. More generally, sensitivity refers to the smallest change in a quantity that an instrument can detect. This can be determined knowing the value of K and the smallest indicated output of the instrument. There are two ranges of the calibration, the input range, xmax - xmin, and the output range, ymax - ymin.
Force-System Resultants and Equilibrium
Published in Richard C. Dorf, The Engineering Handbook, 2018
Primary sensors are based on measurement of external magnetic fields. They find applications in many areas from biological and geophysical measurements to the determination of characteristics of extraterrestrial objects and stars. They also find applications where sensitivity is of utmost importance, as in the case of devices for diagnosing and curing human illnesses. Superconducting quantum interface devices (SQUIDs) and nuclear resonance magnetic imaging (NMR) are examples of such devices. Types of primary sensors discussed in this section include: Magnetodiode and magnetotransistorsMagnetoresistive sensorsMagneto-optical sensorsMagnetic thin filmsHall-effect sensors
An analytically derived reference signal to guarantee safety and comfort in adaptive cruise control systems
Published in Journal of Intelligent Transportation Systems, 2021
Seyed Mehdi Mohtavipour, Morteza Mollajafari
Among these systems, ACC, due to providing safety and comfort for passengers at the same time, is one of the oldest and the most attractive systems for vehicle manufacturers. Earlier, this system was used only in luxury vehicles as an optional system, but due to its significant influence on driving constraints and traffic control, automakers started to leverage this system in both high- and mid-range vehicles (Bishop, 2005; Winner, Witte, Uhler, & Lichtenberg, 1996). The primary version of cruise control systems could only keep the host vehicle at a predefined speed set by the driver. However, when the host vehicle approaches a front vehicle, the driver must manually decrease the speed in order to prevent a collision. Despite this shortcoming, such systems still could be helpful in reducing driving fatigue, especially in highways or non-traffic roads. By installing a distance sensor in the front part of the host vehicle, this system would be converted into an adaptive one. Therefore, it would be able to automatically decrease or increase the host vehicle speed according to its distance from a front vehicle. The most popular distance sensors are millimeter-wave radar (RADAR) and light detection and ranging (LIDAR) (Nagappan, 2005). Also, there are some works which use a front camera instead of RADAR and LIDAR devices (Hofmann, Rieder, & Dickmanns, 2003). However, high sensitivity, accuracy, and cheapness are the main design challenges.
Brain tumor segmentation of normal and lesion tissues using hybrid clustering and hierarchical centroid shape descriptor
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2019
Ravi Shanker, Mahua Bhattacharya
The Dice similarity coefficient (DSC), Sensitivity, Specificity and Accuracy are used for the validation of results as a statistical significant validation metric to measure the performance of spatial overlap accuracy and manual segmentation’s reproducibility for probabilistic segmentation of MR images. A helpful approach is to formulate confusion matrix and validate the results by this matrix. For spatial overlap, the DSC is the main validation metric. The DSC evaluate the overlap between the ground truth and the segmented brain MR image. Sensitivity is another useful measure invalidation that estimates the negligence of false negatives, whereas Specificity quantifies the avoidance of false positives. Accuracy represents the degree to which the segmentation outcome agrees with the ground truth segmented MR images. A sign of reliability depends on high sensitivity test when its result is negative because if the images contain the disease, it rarely misdiagnoses those images (Abdel et al. 2015; Demirhan et al. 2015). The mathematical representation of DSC, sensitivity, specificity and accuracy are as follows:
Family contributions to sport performance and their utility in predicting appropriate referrals to mental health optimization programmes
Published in European Journal of Sport Science, 2019
Julia E. Hussey, Brad Donohue, Kimberly A. Barchard, Daniel N. Allen
ROC Analyses. Differences in sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were examined for the SARI. Specifically, sensitivity represents the ratio of true positive cases over true positive and false negative cases, and specificity is the ratio of true negative cases over true negative and false positive cases (Stojanovic et al., 2014). The PPV is an accuracy statistic that indicates how many identified positive cases actually have the condition in question (e.g. depression), whereas the NPV is the accuracy statistic that indicates how many identified negative cases actually do not have the condition in question (Stojanovic et al., 2014). AUC was used as a measure of the SARI’s ability to distinguish between low concern and high concern athletes, with AUC of 0.50 indicating chance classification and 1.00 indicating perfect classification (Hosmer, Lemeshow, & Sturdivant, 2013). Optimal cut scores were identified using Youden’s Index, which is sensitivity + specificity – 1 (Fluss, Faraggi, & Reiser, 2005).