Personalized Cancer Diagnosis

According to experts, diagnosing cancer takes a long time because it involves reviewing new studies and papers, making it a time-consuming and exhaustive process. With machine learning, we can fast-track the majority of scenarios and help experts get updated details faster.

I have used below classical machine learning algorithms for the problem.

1: Naive Bayes

2: K Nearest Neighbors

3: Logistic Regression

4: Support Vector Machine (SVM)

5: Random Forest Classifier

6: StackedClassifier (Ensemble)

7: MaxVoting Classifier (Ensemble)

As you might know, these algorithms have their own limitations and advantages; I have tried to make the best use of them by addressing their shortcomings:

• The curse of dimensionality has been addressed by Response Coding.
• Class imbalance can be tuned with stratified splits and using the Class weight parameter whenever exploitable.
• Compute-intensive Hyper-Tuning with parallelism when needed.
• Finally, Streamlit’s clean interface and extensive integrations were used to build the demo.