An Explainable Machine Learning Framework for the Accurate Diagnosis of
  Ovarian Cancer
Asif Newaz Abdullah Taharat Md Sakibul Islam A. G. M. Fuad Hasan Akanda
Ovarian cancer (OC) is one of the most prevalent types of cancer in women.
Early and accurate diagnosis is crucial for the survival of the patients.
However, the majority of women are diagnosed in advanced stages due to the lack
of effective biomarkers and accurate screening tools. While previous studies
sought a common biomarker, our study suggests different biomarkers for the
premenopausal and postmenopausal populations. This can provide a new
perspective in the search for novel predictors for the effective diagnosis of
OC. Lack of explainability is one major limitation of current AI systems. The
stochastic nature of the ML algorithms raises concerns about the reliability of
the system as it is difficult to interpret the reasons behind the decisions. To
increase the trustworthiness and accountability of the diagnostic system as
well as to provide transparency and explanations behind the predictions,
explainable AI has been incorporated into the ML framework. SHAP is employed to
quantify the contributions of the selected biomarkers and determine the most
discriminative features. A hybrid decision support system has been established
that can eliminate the bottlenecks caused by the black-box nature of the ML
algorithms providing a safe and trustworthy AI tool. The diagnostic accuracy
obtained from the proposed system outperforms the existing methods as well as
the state-of-the-art ROMA algorithm by a substantial margin which signifies its
potential to be an effective tool in the differential diagnosis of OC.