Early detection of liver disease using data visualisation and classification method

Abstract Detection of early-stage liver diseases is a challenge in medical field. Automated diagnostics based on machine learning therefore could be very important for liver tests of patients. This paper investigates 225 liver function test records (each record include 14 features), which is a subset from 1000 patients’ liver function test records that include the records of 25 patients with liver disease from a community hospital. We combine support vector data description (SVDD) with data visualisation techniques and the glowworm swarm optimisation (GSO) algorithm to improve diagnostic accuracy. The results show that the proposed method can achieve 96% sensitivity, 86.28% specificity, and 84.28% accuracy. The new method is thus well-suited for diagnosing early liver disease.

[1]  Heeyoul Choi,et al.  Robust kernel Isomap , 2007, Pattern Recognit..

[2]  Heikki Mannila,et al.  Random projection in dimensionality reduction: applications to image and text data , 2001, KDD '01.

[3]  Shapla Rani Ghosh,et al.  A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis , 2016 .

[4]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[5]  Yasuyuki Okamoto,et al.  Application of neural network to the interpretation of laboratory data for the diagnosis of two forms of chronic active hepatitis , 1996 .

[6]  Lawrence K. Saul,et al.  Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold , 2003, J. Mach. Learn. Res..

[7]  Nilanjan Ray,et al.  Pattern Recognition Letters , 1995 .

[8]  Robert P. W. Duin,et al.  Support vector domain description , 1999, Pattern Recognit. Lett..

[9]  Chun-Ling Chuang,et al.  A hybrid diagnosis model for determining the types of the liver disease , 2010, Comput. Biol. Medicine.

[10]  John Riedl,et al.  Application of Dimensionality Reduction in Recommender System - A Case Study , 2000 .

[11]  Han Guang,et al.  Traversable Region Detection Based on One-class SVM and Multi-visual Features Fusion , 2011 .

[12]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[13]  Marek J. Druzdzel,et al.  A Probabilistic Causal Model for Diagnosis of Liver Disorders , 2005 .

[14]  Kemal Polat,et al.  A new medical decision making system: Least square support vector machine (LSSVM) with Fuzzy Weighting Pre-processing , 2007, Expert Syst. Appl..

[15]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[16]  Rong-Ho Lin,et al.  An intelligent model for liver disease diagnosis , 2009, Artif. Intell. Medicine.

[17]  U. Rajendra Acharya,et al.  ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform , 2013, Biomed. Signal Process. Control..

[18]  Kemal Polat,et al.  Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism , 2007, Expert Syst. Appl..

[19]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[20]  Young Sun Kim,et al.  Screening test data analysis for liver disease prediction model using growth curve. , 2003, Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie.

[21]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[22]  Min Jung Lee,et al.  Human liver carboxylesterase 1 outperforms alpha‐fetoprotein as biomarker to discriminate hepatocellular carcinoma from other liver diseases in Korean patients , 2013, International journal of cancer.

[23]  Zhan Yong-zhao,et al.  Support vector data description discriminant analysis , 2011 .

[24]  Jing Wang,et al.  Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Debasish Ghose,et al.  Detection of multiple source locations using a glowworm metaphor with applications to collective robotics , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[26]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[27]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[28]  Robert P. W. Duin,et al.  Support Vector Data Description , 2004, Machine Learning.

[29]  N. B. Venkateswarlu,et al.  A Critical Comparative Study of Liver Patients from USA and INDIA: An Exploratory Analysis , 2012 .

[30]  D. Donoho,et al.  Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data , 2003, Proceedings of the National Academy of Sciences of the United States of America.