An Evolutionary Computation Approach for Optimizing Multilevel Data to Predict Patient Outcomes

<div class="floats-full-page floats-full-page-other"><h2>Table 4</h2><div class="floats-full-page-caption"><div>Summary table of genetic algorithm control parameters and operators.</div></div><div class="floats-full-page-content"><table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr /></td></tr><tr class="thead"><td class="align_left">Parameter</td><td class="align_center">Setting</td></tr><tr><td class="thead-hr" colspan="2"><hr /></td></tr><tr><td class="align_left">Population size (<i>N</i>)</td><td class="align_center">40</td></tr><tr><td class="align_left">Number of generations</td><td class="align_center">100</td></tr><tr><td class="align_left">Selection</td><td class="align_center">Tournament (<i>k</i> = 3)</td></tr><tr><td class="align_left">Crossover operation</td><td class="align_center">Uniform</td></tr><tr><td class="align_left">Crossover rate</td><td class="align_center">0.6</td></tr><tr><td class="align_left">Mixing ratio</td><td class="align_center">0.2</td></tr><tr><td class="align_left">Mutation operation</td><td class="align_center">Bit flip</td></tr><tr><td class="align_left">Mutation rate</td><td class="align_center">0.2</td></tr><tr><td class="align_left">Bit flip rate</td><td class="align_center">0.05</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr" /></td></tr></table></td></tr></table></div></div>

Table 4 | An Evolutionary Computation Approach for Optimizing Multilevel Data to Predict Patient Outcomes

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Table 4: An Evolutionary Computation Approach for Optimizing Multilevel Data to Predict Patient Outcomes

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Table 4

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<p><em>Journal of Healthcare Engineering </em>provides a vehicle for the exchange of advanced knowledge, emerging technologies, and innovative ideas related to all aspects of engineering involved in healthcare delivery processes and systems.</p>


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