Machine Learning Techniques for Medical Diagnosis: a Review

Machine learning algorithm can significantly help in solving the healthcare problems by developing classifier systems that can assist physicians in diagnosing and predicting diseases in early stages. However, extracting knowledge from medical data is challenging as this data may be heterogeneous, unorganized, and high dimensional and may contain noise and outliers. Most appropriate method can be chosen only after analyzing all the available machine learning techniques and validating their performances in terms of accuracy and comprehensibility. This literature has reviewed the use of machine learning algorithms like decision tree, support vector machine, random forest, evolutionary algorithms and swarm intelligence for accurate medical diagnosis. The dependence on medical images for diagnosing a disease is on rise. Since interpreting modern medical images is becoming increasingly complex, machine learning algorithms in medical imaging can provide significant assistance in medical diagnosis. Machine learning techniques could be used for large scale and complex biological data analysis as these techniques are efficient and inexpensive in solving bioinformatics problems.