Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques

Algorithm 1

Boruta algorithm.

The steps in the Boruta algorithm are as follows:

Step 1: Enhance the data scheme by replicating all factors (so if the original collection has fewer than five features, the data schemes are often prolonged from at least five shadow features).

Step 2: Eliminate the additional features’ correlation coefficients with the reaction by shuffling them.

Step 3: On the extensive data system, operate a random forest classifier and collect the rankings.

Step 4: Determine the shadow feature with the highest score (MZSA), after which allocate a hit to every characteristic that outperformed MZSA.

Step 5: Using the MZSA, initiate a two-test of fairness for every factor of unknown significance.

Step 6: Sign features less importance than MZSA as “insignificant” and eliminates individuals from the data repository forever.

Step 7: Consider the characteristics that have greater significance than MZSA to be “significant.”

Step 8: Deactivate all shadow effects.

Step 9: Repeat the above process 9 when all of the characteristics have been allotted significance or the method has achieved the random forest run restriction that was initially established.