Improving the Accuracy of Medical Diagnosis Detection using Machine Learning

Medical diagnosis is a key step in delivering best healthcare and it has to be accurate and timely. Despite that, conventional diagnostic methods are imperfect; there is human error, inefficient data analysis, and scalability issues. In this research, we present a machine learning based framework to improve accuracy of medical diagnosis detection by means of combining data preprocessing in a robust manner, feature optimization, and an algorithmic modelling. To facilitate high quality input to the machine learning models, the proposed system makes use of Python based tools and libraries to pre process raw medical datasets, eliminate inconsistencies, normalize features, and extract useful attributes. As the core predictive algorithm, Support Vector Machines (SVM), have shown to be highly effective in classification tasks, are used. The design is modular system and provides seamless data cleaning, prediction, and evaluation process integration which leads to scalability between health care domain. Benchmark datasets were used for experimental validation which showed the superior diagnostic accuracy, precision and reliability compared to both existing models and other previously demonstrated methods. In summary, the work presented in this project is a next step towards making hospitalization healthcare diagnostics data driven, scalable, and intelligent decision support systems in order to improve patient care and outcomes.