Investigation of machine learning algorithms for the pre-estimating medical diagnosis.

Heart Disease, Diabetes and lung cancer are complex diseases and globally many people are suffering from these diseases. On time and efficient identification of these diseases, play a vital role in health care monitoring systems. In this article, we proposed an effective and precise framework to determine these and the framework depends on Machine Learning algorithms. The system is developed based on classification algorithms and includes Support vector machine, Logistic regression, K-nearest neighbor, Decision tree, Random Forest and XGBoost Classifiers. Firstly, Kaggle dataset is considered for testing the data. Later pre-processing of data is done. Then the greatest piece of any data analysis project is ensuring that the information is effectively organized and fixing it when it isn't. The initial segment of this interaction is managing Missing Data. After dealing with the missing data, we are ready to start formatting the data set for making classification tree. Formatting data is splitting data into dependent and independent variables. Finally, after formatting the data for classification tree, the data is split into training and testing sets. The experiments results show that the classifier Random Forest tree achieved high accuracy as compared to other classifiers. Additionally, the proposed system can be easily implemented in healthcare for identifying heart disease, diabetes and lung cancer. [ABSTRACT FROM AUTHOR]