Genetic algorithms for spectral pattern recognition

et al. 2010

The relationship between the mortality of cervical cancer and soil trace elements of 23 regions of China was investigated. A total of 25 elements (i.e., Na, K, Mg, Ca, Sr, Hg, Pb, B, Tm, Th, U, Sn, Hf, Bi, Ta, Te, Mo, Br, I, As, Cr, Cu, Fe, Zn, and Se) were considered. First, 23 samples were split into the training set with 12 samples and the test set with 11 samples. Then, a combination strategy called genetic algorithm-partial least squares (GA-PLS) was used to pick out five important elements. i.e., Br, Ta, Pb, Cr, and As. Afterwards, the classic partial least squares (PLS) model and least square support vector machine (LSSVM) model were developed and compared. The results revealed that the SVM model significantly outperforms the PLS model, indicating that the combination of GA-PLS and LSSVM can serve as a potential tool for predicting the mortality of cancer based on trace elements.

Section: Genetic Algorithmmentioning

confidence: 99%

Modeling the Relationship Between Cervical Cancer Mortality and Trace Elements Based on Genetic Algorithm–Partial Least Squares and Support Vector Machines

et al. 2010

“…Genetic Algorithm GA [9,10] is efficient numerical optimization method based on the principle of genetics and natural selection. The ultimate goal of the genetic algorithms is the optimization of a given response function.…”

Section: Theory and Algorithmmentioning

confidence: 99%

The Prediction of Nasopharyngeal Carcinoma Mortality Based on Soil Element Levels in China

et al. 2010

The relationship between the mortality of nasopharyngeal carcinoma (NPC) and soil trace elements of 29 regions of China was investigated. A total of 29 elements (i.e., Mn, Na, K, Mg, Ca, Sr, Ba, Hg, Pb, Se, In, Yb, Lu, Th, U, Sn, Ti, Zr, Hf, Bi, Ta, Te, Br, I, As, Cr, Cu, Fe, and Zn) were considered. A hybrid strategy called genetic algorithm-partial least squares was used to screen out important elements. As a result, only six elements, i.e., Mn, Ti, Mg, K, Na, and I, were picked out, based on which, a PLS model containing two latent variables exhibited the best performance. According to whether the mortality is larger than 2/100,000 (2 × 10(-5)), all the 29 regions were divided into the low-mortality group with 23 regions and the high-mortality group with six regions. Based on the optimal PLS model, all high-mortality regions were successfully classified while only two low-mortality regions were misclassified, i.e., an accuracy of 93%, implying that the selected six elements are effective and successful for predicting the NPC mortality of a region.

“…A detailed description of the genetic algorithms (GA) can be found in the literature [29][30][31]. GA are simulated methods based on ideas from Darwin's theory of natural selection and evolution (the struggle for life).…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Analysis of the Relationship Between Leukemia Mortality and Soil Trace Elements using Chemometrics

Xia

2009

The relationship between the mortality of leukemia and the contents of trace elements in the soils of 29 regions of China was investigated. A total of 27 elements were determined for each region. Considering that an efficient variable selection can be highly beneficial both to improve the predictive ability of the model and to greatly reduce its complexity, genetic algorithm-partial least squares was used to screen out 13 qualified elements. As a result, only 13 elements, i.e., As, Hg, Mn, Sr, Ba, Cu, Ti, Co, K, Ca, Rb, Zn, and Mg, were picked out and, a partial least squares model with three latent variables was obtained, whose prediction exhibited a correlative coefficient of 0.874 with actual mortality. Especially, it showed a high negative correlation between the content of soil As and the mortality of leukemia. Such a fact can be explained by the apoptotic effect of cancerous cells by trace-amount arsenic trioxide. Furthermore, according to whether the mortality was larger than two out of 100,000 (2 × 10⁻⁵), all the 29 regions were divided into 21 high-mortality regions and eight low-mortality regions and were assigned the label -1 or 1, respectively. Using the same 13 elements, a Fisher's discriminant analysis model was developed, which can successfully discriminate low- and high-mortality groups.