Machine Learning in healthcare can be applied to digitally prognose (predictive diagnosis) using the risk factors of a disease. ML can detect patterns of certain diseases with patient electronic health records and report anomalies to the pattern. ML Diagnostic applications are increasingly being used to supplement clinicians’ decisions, using data lakes to condensify millions of observations of diseases. To instantiate the power of machine learning as a medical prognostic tool, I examined its use in cervical cancer to classify one’s risk of having it, using risk factors, using a publicly available Cervical Cancer Risk Classification Data Set

Characteristics of benign and malignant tumours

This data set consists of several instances of tumors. Tumors can either be benign (non-cancerous) or malignant (cancerous). Benign tumors grow locally and do not spread. As a result, they are not considered cancerous. However, they can still pose a danger, especially if they press against vital organs like the brain. Malignant tumors, in contrast, have the ability to spread and invade other tissues. This process, known as metastasis, is a key feature of cancer. There are many different types of malignancy-based tumors as well as locations that this type of cancer tumor can originate, as described in the data set specification.

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