Application of Machine Learning Methods to Medical Diagnosis

The technological boom in recent years has come with great advances in medical technologies. New technologies, such as MRI (magnetic resonance imaging) and ECG (electrocardiogram), enable better understanding of the functions and malfunctions of the human body. However, technological progress adds pressure on medical professionals, who are now faced with an influx of data to interpret. Moreover, inundated with data, doctors may be more prone to mistakes. Misdiagnosis, though seemingly rare, is actually quite common. A recent study of Patient Safety in American Hospitals by HealthGrades found that per 1,000 patients, more than 150 were classifi ed as “failure to rescue,” meaning a failure to diagnose correctly or in time. Twenty percent of patients in the emergency department (ED) are misdiagnosed, according to reports at http://wrongdiagnosis.com. In addition, John Davenport, in his paper “Documenting High-Risk Cases to Avoid Malpractice Liability,” observes that the majority of the cases of misdiagnosis occurs in serious diseases such as breast cancer, appendicitis, and colon cancer. Clearly, from these statistics, it can be concluded that misdiagnosis is a signifi cant problem. Thus, it is necessary to fi nd an effi cient, unbiased, and accurate method for diagnosis. Machine learning computer aided diagnostics (CAD) provides a realistic solution to the problem of misdiagnosis. Though the intuition of a human can never be replaced, machine learning can provide a useful secondary opinion to help in the diagnosis of a patient. In CAD applications, empirical data from various medical studies are used to estimate a predictive diagnostic model, which can be used for diagnosing new patients. The simplest type of predictive problem is classifi cation. In a classifi cation setting, the goal is to estimate a model that classifi es patients’ data into two classes (e.g., healthy and sick) based on available features of each patient. The input features may include clinical data (e.g., the number of lymph nodes, results of a blood test), demographic data (e.g., age and sex), genomic data, etc. Often, the number of input variables d is large, say, d =10 inputs or 100 inputs, and can even reach hundreds of thousands of variables in genomic data. Current medical technologies, such as heart monitors, take numerous readings on several characteristics each minute. The scenario here is limited, but future work would explore several of the topics considered previously. Due to the large number of input variables, designing a classifi er amounts to estimating a decision boundary in a high-dimensional space based on available diagnostic data about past patients. This is a diffi cult task for medical doctors because (a) humans have no intuition of working in/ visualizing a high-dimensional input space and (b) there may be many models (decision boundaries) that explain available historical data equally well, but have different prediction accuracies. Machine learning methods address both problems and enable estimation of statistically reliable classifi cation models for medical diagnosis. Once such a predictive model is estimated, it can be used in future diagnosis (classifi cation) of new patients. Prediction refers to the CAD model assigning the diagnosis to a new patient, based on the values of input features for this patient. Two binary classifi cation methods are k-nearest neighbors (kNN) and support vector machine (SVM) classifi er. The kNN method is a simple classical method based on the intuitive idea of classifying a new patient based on his/her similarity to other patients (with known classifi cation labels). The SVM method is a more recent technology that has become widely used since the late 1990s. It is based on a solid theoretical foundation of statistical learning theory and uses a new concept (i.e., margin) to control the prediction accuracy.