Enhancing disease prediction accuracy in the healthcare industry using SVM based machine learning techniques

Abstract

Data analytics is the process of identifying new insights from the newlinedata. Data analytics in the healthcare industry helps identify new patterns in newlinethe disease, provide personalized healthcare facilities, and diagnose and newlinepredict the disease. newlineIn developing countries like India, healthcare data analytics are newlinehelpful for remote people to access the medical facilities efficiently and newlinereduce human errors during disease diagnosis using Machine Learning newlinealgorithms. It improves the disease prediction accuracy by selecting newlineappropriate Machine Learning algorithms in the healthcare data analytics. newlineA Decision Support System (DSS) is proposed to predict and improve the newlineprediction accuracy using Machine Learning algorithms. This thesis addresses newlinethe need for DSS to predict the disease, identify optimal features, and newlineoptimise the predictive algorithm to improve the disease prediction accuracy newlinein the healthcare industry. newlineThe central argument of this thesis is to understand the working newlineprocedure of various machine learning algorithms and to employ different newlinefeature selection techniques in the healthcare industry. The research work newlinefocuses on the design of DSS to predict diseases in the healthcare industry newlineand exhibit the opportunities to improve the prediction accuracy at the feature newlineselection level and predictive model levels. A Feature Selection model and newlineSVM based Improved Radial-Bias techniques were proposed in this research newlinework. newline