Download Computational Intelligence Techniques in Health Care by P.V. Lakshmi, Wengang Zhou, P Satheesh PDF

By P.V. Lakshmi, Wengang Zhou, P Satheesh

This e-book offers study on rising computational intelligence concepts and instruments, with a selected concentrate on new developments and purposes in well-being care. Healthcare is a multi-faceted area, which contains complicated decision-making, distant tracking, healthcare logistics, operational excellence and sleek info structures. in recent times, using computational intelligence tips on how to handle the size and the complexity of the issues in healthcare has been investigated. This e-book discusses quite a few computational intelligence tools which are applied in functions in numerous parts of healthcare. It contains contributions through practitioners, expertise builders and resolution providers.

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Accuracy is the percentage of correctly labeled tuples to the summation of correctly and incorrectly labeled tuples. Sensitivity is the percentage of truly labeled positives to the summation of positives labeled as positives and positives incorrectly labeled as negatives. Specificity is the percentage of true negatives to the summation of true negatives and false positives. Recall is same as sensitivity, precision is the percentage of correctly labelled positives to the total of positives and negatives labeled as positives.

Cross-validation was calculated using 2 random trials with F to leave and F to enter being 2 in F stepping to include the most significant variables. 442 (continued) 48 G. Nirmala et al. 180 (continued) 50 G. Nirmala et al. 3 51 Molecular Descriptors Molecular descriptors selected in the study are: topological, shape and connectivity indices, total dipole and lipole, molecular weight, h-bond donors, h-bond acceptors, logP and rotatable bond counts, heat of formation and electrostatic properties like HOMO (Highest Occupied Molecular Orbital), LUMO (Lowest Unoccupied Molecular Orbital).

Step 11: Update the best pbest and gbest. Step 12: Update particle position based on Eq. 3 and velocity based on Eqs. 2 Step 13: Store the pbest and gbest values in previous pbest and gbest. Step 14: Update gbest as best rules. Step 15: Perform rule pruning. Step 16: Apply pruned rules on test data. Step 17: Calculate accuracy. Step 18: Stop. Stopping criteria: • If the iterations are greater than the maximum iterations specified. 4 Dataset Description For this work, we considered the original Wisconsin breast cancer dataset, Mammographic Mass Data.

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