Prediction of Chronic and Infectious Diseases using Machine Learning Classifiers- A Systematic Approach

Abstract

Infectious and chronic diseases devastate millions of people across the world each year. Nonetheless, each type of disease substantiates differently. According to the National Centre for Health Statistics, USA, Infectious diseases or communicable diseases are the ones based on the cause, which spreads from person to person or animal to person caused by microorganisms such as bacteria or parasite and can be cured. Chronic diseases are based on the effect, which may have the origin of infectious disease, prolonged to three or more months, doesn't spread from one person to another and cannot be cured. Some chronic diseases such as cervical cancer and liver cancer have originated from infectious diseases such as human papillomavirus (HPV) and hepatitis B, C virus. This paper focuses on various machine learning classification techniques in predicting chronic diseases such as Cardio Vascular Disease (CVD), Chronic Kidney Disease (CKD), lung cancer, and infectious diseases such as hepatitis and dengue serotypes. In the analysis, ABC4.5 classifier outperformed with accuracy of 92.76 % than the other classifiers in predicting Chronic Kidney Disease (CKD), Random Forest classifier achieved an accuracy of 90.32% which is higher than Logistic regression of accuracy 83.87% in predicting hepatitis. Hoeffding classifier achieves an accuracy of 88.56% which is higher than the other classifier in predicting Cardio Vascular Disease. Multi swarm optimized Multilayer perceptron achieved an accuracy of 85.18% which is higher than the particle swarmed optimized multilayer perceptron in predicting dengue serotypes. Artificial Neural Network (ANN) classifier outperformed all the classifiers under analysis with an accuracy of 93.00 % in predicting lung cancer.

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