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Development and implementation of a web-based system to study children with malnutrition

Syed-Mohamad SM

Comput Methods Programs Biomed, 2009 Jan;93(1):83-92.
PMID: 18789553 DOI: 10.1016/j.cmpb.2008.07.011

To develop and implement a collective web-based system to monitor child growth in order to study children with malnutrition.

Matched MeSH terms: Diagnosis, Computer-Assisted/statistics & numerical data*
A new machine learning technique for an accurate diagnosis of coronary artery disease

Abdar M, Książek W, Acharya UR, Tan RS, Makarenkov V, Pławiak P

Comput Methods Programs Biomed, 2019 Oct;179:104992.
PMID: 31443858 DOI: 10.1016/j.cmpb.2019.104992

BACKGROUND AND OBJECTIVE: Coronary artery disease (CAD) is one of the commonest diseases around the world. An early and accurate diagnosis of CAD allows a timely administration of appropriate treatment and helps to reduce the mortality. Herein, we describe an innovative machine learning methodology that enables an accurate detection of CAD and apply it to data collected from Iranian patients.
METHODS: We first tested ten traditional machine learning algorithms, and then the three-best performing algorithms (three types of SVM) were used in the rest of the study. To improve the performance of these algorithms, a data preprocessing with normalization was carried out. Moreover, a genetic algorithm and particle swarm optimization, coupled with stratified 10-fold cross-validation, were used twice: for optimization of classifier parameters and for parallel selection of features.
RESULTS: The presented approach enhanced the performance of all traditional machine learning algorithms used in this study. We also introduced a new optimization technique called N2Genetic optimizer (a new genetic training). Our experiments demonstrated that N2Genetic-nuSVM provided the accuracy of 93.08% and F1-score of 91.51% when predicting CAD outcomes among the patients included in a well-known Z-Alizadeh Sani dataset. These results are competitive and comparable to the best results in the field.
CONCLUSIONS: We showed that machine-learning techniques optimized by the proposed approach, can lead to highly accurate models intended for both clinical and research use.

Matched MeSH terms: Diagnosis, Computer-Assisted/statistics & numerical data
Fulltext Multistage Optimization Using a Modified Gaussian Mixture Model in Sperm Motility Tracking

Alameri M, Hasikin K, Kadri NA, Nasir NFM, Mohandas P, Anni JS, et al.

Comput Math Methods Med, 2021;2021:6953593.
PMID: 34497665 DOI: 10.1155/2021/6953593

Infertility is a condition whereby pregnancy does not occur despite having unprotected sexual intercourse for at least one year. The main reason could originate from either the male or the female, and sometimes, both contribute to the fertility disorder. For the male, sperm disorder was found to be the most common reason for infertility. In this paper, we proposed male infertility analysis based on automated sperm motility tracking. The proposed method worked in multistages, where the first stage focused on the sperm detection process using an improved Gaussian Mixture Model. A new optimization protocol was proposed to accurately detect the motile sperms prior to the sperm tracking process. Since the optimization protocol was imposed in the proposed system, the sperm tracking and velocity estimation processes are improved. The proposed method attained the highest average accuracy, sensitivity, and specificity of 92.3%, 96.3%, and 72.4%, respectively, when tested on 10 different samples. Our proposed method depicted better sperm detection quality when qualitatively observed as compared to other state-of-the-art techniques.

Matched MeSH terms: Diagnosis, Computer-Assisted/statistics & numerical data
A new hybrid intelligent system for accurate detection of Parkinson's disease

Hariharan M, Polat K, Sindhu R

Comput Methods Programs Biomed, 2014 Mar;113(3):904-13.
PMID: 24485390 DOI: 10.1016/j.cmpb.2014.01.004

Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.

Matched MeSH terms: Diagnosis, Computer-Assisted/statistics & numerical data
An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding

Saleh MD, Eswaran C

Comput Methods Biomech Biomed Engin, 2012;15(5):517-25.
PMID: 21331960 DOI: 10.1080/10255842.2010.545949

Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.

Matched MeSH terms: Diagnosis, Computer-Assisted/statistics & numerical data