Title |
Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease
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Published in |
BMC Bioinformatics, December 2014
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DOI | 10.1186/1471-2105-15-s16-s11 |
Pubmed ID | |
Authors |
Piers Johnson, Luke Vandewater, William Wilson, Paul Maruff, Greg Savage, Petra Graham, Lance S Macaulay, Kathryn A Ellis, Cassandra Szoeke, Ralph N Martins, Christopher C Rowe, Colin L Masters, David Ames, Ping Zhang |
Abstract |
Assessment of risk and early diagnosis of Alzheimer's disease (AD) is a key to its prevention or slowing the progression of the disease. Previous research on risk factors for AD typically utilizes statistical comparison tests or stepwise selection with regression models. Outcomes of these methods tend to emphasize single risk factors rather than a combination of risk factors. However, a combination of factors, rather than any one alone, is likely to affect disease development. Genetic algorithms (GA) can be useful and efficient for searching a combination of variables for the best achievement (eg. accuracy of diagnosis), especially when the search space is large, complex or poorly understood, as in the case in prediction of AD development. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 50% |
Scientists | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Germany | 1 | <1% |
Australia | 1 | <1% |
Unknown | 122 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 21 | 17% |
Student > Master | 20 | 16% |
Researcher | 16 | 13% |
Student > Bachelor | 10 | 8% |
Other | 5 | 4% |
Other | 16 | 13% |
Unknown | 36 | 29% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 15 | 12% |
Psychology | 12 | 10% |
Engineering | 11 | 9% |
Agricultural and Biological Sciences | 7 | 6% |
Biochemistry, Genetics and Molecular Biology | 6 | 5% |
Other | 30 | 24% |
Unknown | 43 | 35% |