Some algorithms and approach for identification of specific patterns?
I suggest , go for Anomaly detection:

Anomaly Detection is done assuming our data has a probability distribution(gaussian). We can plot data to see if thats the case, if not we can make it gaussian using log transforms. Gaussian distribution specifies the regions and probabilities of our data lying in those regions.

For example : replace original feature x -> Log(x) or feature x -> (x)^4/3 etc..

Also regarding the threshold value which decides outliers you can play with it and see that with Higher threshold you will be rejecting more entries and this might be required where doctors are trying to isolate cancer patients amongst many normal ones without taking any risk/chances. Again outliers here doesn't mean cancer patient but definitely worth a medical test. And you can set it to lower value if you are getting too many normal data flagged as outliers.

We have skewed data sets since we have more examples of one kind than the other. For example when we get air craft engine data we might just have data for few bad ones and mostly for good ones.Use of cross validation data is suggested.F1-Score is a pretty good metric to evaluate the performance of the algorithm.

To get a proper hold on this topic , I also suggest go through  anomaly detection course videos by Andrew NG in machine learning on Coursera. Free course and very nicely made.



The well known techniques for the same are:
Density-based techniques (k-nearest neighbor),Support vector Machines,Cluster Analysis Anomaly detection. People also use Fuzzy logic based techniques.
And for python I suggest use libraries like scikit-learn and TensorFlow , they have superb implementations of usable machine learning algorithms.Heres a video link from a googler,giving general overview for the same :https://www.youtube.com/channel/UCwUtgW0JCPFjLbKx4VVr3bw