Machine learning offers a wide range of assistance in healthcare especially in the early diagnosis of diseases. However, these models have a constraint in terms of time and processing complexity as it involves complex computations. These issues can be addressed through the evolution of quantum in the computing field. Quantum algorithms are constructed to handle computations for linear algebra and neural networks for a betterment of many AI-based applications. In this work, we investigate quantum machine learning algorithm for classifying medical features which are extracted from the fine needle aspirated tissues. The quantum support vector machine model is employed on the WDBC dataset which achieved an exponential speedup over its conventional alternatives.