Liver disease detection using machine learning techniques

Around a million deaths occur due to liver diseases globally. There are several traditional methods to diagnose liver diseases, but they are expensive. Early prediction of liver disease would benefit all individuals prone to liver diseases by providing early treatment. As technology is growing in health care, machine learning significantly affects health care for predicting conditions at early stages. This study finds how accurate machine learning is in predicting liver disease. This present study introduces the liver disease prediction (LDP) method in predicting liver disease that can be utilised by health professionals, stakeholders, students and researchers. Five algorithms, namely Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbors (K-NN), Linear Discriminant Analysis (LDA), and Classification and Regression Trees (CART), are selected. The accuracy is compared to uncover the best classification method for predicting liver disease using R and Python. From the results, K-NN obtains the best accuracy with 91.7%, and the autoencoder network achieved 92.1% accuracy, which is above the acceptable level of accuracy and can be considered for liver disease prediction.tru

IMPROVING CORONARY HEART DISEASE PREDICTION BY OUTLIER ELIMINATION

Nowadays, heart disease is the major cause of deaths globally. According to a survey conducted by the World Health Organization, almost 18 million people die of heart diseases (or cardiovascular diseases) every day. So, there should be a system for early detection and prevention of heart disease. Detection of heart disease mostly depends on the huge pathological and clinical data that is quite complex. So, researchers and other medical professionals are showing keen interest in accurate prediction of heart disease.  Heart disease is a general term for a large number of medical conditions related to heart and one of them is the coronary heart disease (CHD). Coronary heart disease is caused by the amassing of plaque on the artery walls. In this paper, various machine learning base and ensemble classifiers have been applied on heart disease dataset for efficient prediction of coronary heart disease. Various machine learning classifiers that have been employed include k-nearest neighbor, multilayer perceptron, multinomial naïve bayes, logistic regression, decision tree, random forest and support vector machine classifiers. Ensemble classifiers that have been used include majority voting, weighted average, bagging and boosting classifiers. The dataset used in this study is obtained from the Framingham Heart Study which is a long-term, ongoing cardiovascular study of people from the Framingham city in Massachusetts, USA. To evaluate the performance of the classifiers, various evaluation metrics including accuracy, precision, recall and f1 score have been used. According to our results, the best accuracy was achieved by logistic regression, random forest, majority voting, weighted average and bagging classifiers but the highest accuracy among these was achieved using weighted average ensemble classifier.&nbsp