Machine learning for improved diagnosis and prognosis in healthcare

Machine learning has gained tremendous interest in the last decade fueled by cheaper computing power and inexpensive memory — making it efficient to store, process and analyze growing volumes of data. Enhanced algorithms are being designed and applied on large datasets to help discover hidden insights and correlations amongst data elements not obvious to human. These insights help businesses take better decisions and optimize key indicators of interest. The growing popularity of machine learning also stems from the fact that learning algorithms are agnostic to the domain of application. Classification algorithms, for example, that could be applied to categorize faults in windmill blades can also be used for categorizing TV viewers in a survey. The actual value of machine learning however depends on the ability to adapt and apply these algorithms to solve specific real world problems. In this paper we discuss two such applications for interpreting medical data for automated analysis. Our first case study demonstrates the use of Bayesian Inference, a paradigm of machine learning, for diagnosing Alzheimer's disease based on cognitive test results and demographic data. In the second case study we focus on automated classification of cell images to determine the advancement and severity of breast cancer using artificial neural networks. Although these research are still preliminary, they demonstrate the value of machine learning techniques in providing quick, efficient and automated data analysis. Machine learning offers hope with early diagnosis of diseases, help patients in making informed decisions on treatment options and can help in improving overall quality of their lives.