Accurate telemonitoring of Parkinson's disease diagnosis using robust inference system

A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets

Parkinson's Disease (PD) is a prevalent neurodegenerative disorder with significant clinical implications. Early and accurate diagnosis of PD is crucial for timely intervention and personalized treatment. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as promis-ing tools for improving PD diagnosis. This review paper presents a detailed analysis of the current state of ML and DL-based PD diagnosis, focusing on voice, handwriting, and wave spiral datasets. The study also evaluates the effectiveness of various ML and DL algorithms, including classifiers, on these datasets and highlights their potential in enhancing diagnostic accuracy and aiding clinical decision-making. Additionally, the paper explores the identifi-cation of biomarkers using these techniques, offering insights into improving the diagnostic process. The discussion encompasses different data formats and commonly employed ML and DL methods in PD diagnosis, providing a comprehensive overview of the field. This review serves as a roadmap for future research, guiding the development of ML and DL-based tools for PD detection. It is expected to benefit both the scientific community and medical practitioners by advancing our understanding of PD diagnosis and ultimately improving patient outcomes.

Studying Pregnancy Outcome Risk in Patients with Systemic Lupus Erythematosus Based on Cluster Analysis

Background. Pregnancy in systemic lupus erythematosus (SLE) patients is a challenge due to the potential maternal and fetal complications. Therefore, a multidisciplinary assessment of disease risk before and during pregnancy is essential to improve pregnancy outcomes. Objectives. Our purpose was to (i) define clusters of patients with similar history and laboratory features and determine the associative maternal and perinatal outcomes and (ii) evaluate the risk spectrum of maternal and perinatal outcomes of pregnancy in SLE patients, represented by our established risk-assessment chart. Methods. Medical records of 119 patients in China were analyzed retrospectively. Significant variables with $p<0.05$ were selected. The self-organizing map was used for clustering the data based on historical background and laboratory features. Results. Clustering was conducted using 21 maternal and perinatal features. Five clusters were recognized, and their prominent maternal manifestations were as follows: cluster 1 (including 27.73% of all patients): preeclampsia and lupus nephritis; cluster 2 (22.69%): oligohydramnios, uterus scar, and femoral head necrosis; cluster 3 (13.45%): upper respiratory tract infection; cluster 4 (15.97%): premature membrane rupture; and cluster 5 (20.17%): no problem. Conclusion. Pregnancy outcomes in SLE women fell into three categories, namely high risk, moderate risk, and low risk. Present manifestations, besides the medical records, are a potential assessment means for better management of pregnant SLE patients.

Machine Learning Models for Diagnosis of Parkinson's Disease Using Multiple Structural Magnetic Resonance Imaging Features

Purpose

This study aimed to develop machine learning models for the diagnosis of Parkinson's disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance.

Methods

Brain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson's Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson's correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets.

Results

After feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong's test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models.

Conclusion

The combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.

Design of an integrated model for diagnosis and classification of pediatric acute leukemia using machine learning

Applying artificial intelligence techniques for diagnosing diseases in hospitals often provides advanced medical services to patients such as the diagnosis of leukemia. On the other hand, surgery and bone marrow sampling, especially in the diagnosis of childhood leukemia, are even more complex and difficult, resulting in increased human error and procedure time decreased patient satisfaction and increased costs. This study investigates the use of neuro-fuzzy and group method of data handling, for the diagnosis of acute leukemia in children based on the complete blood count test. Furthermore, a principal component analysis is applied to increase the accuracy of the diagnosis. The results show that distinguishing between patient and non-patient individuals can easily be done with adaptive neuro-fuzzy inference system, whereas for classifying between the types of diseases themselves, more pre-processing operations such as reduction of features may be needed. The proposed approach may help to distinguish between two types of leukemia including acute lymphoblastic leukemia and acute myeloid leukemia. Based on the sensitivity of the diagnosis, experts can use the proposed algorithm to help identify the disease earlier and lessen the cost.

Deep Learning Approach to Parkinson's Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification

This study presents an approach to Parkinson's disease detection using vowels with sustained phonation and a ResNet architecture dedicated originally to image classification. We calculated spectrum of the audio recordings and used them as an image input to the ResNet architecture pre-trained using the ImageNet and SVD databases. To prevent overfitting the dataset was strongly augmented in the time domain. The Parkinson's dataset (from PC-GITA database) consists of 100 patients (50 were healthy / 50 were diagnosed with Parkinson's disease). Each patient was recorded 3 times. The obtained accuracy on the validation set is above 90% which is comparable to the current state-of-the-art methods. The results are promising because it turned out that features learned on natural images are able to transfer the knowledge to artificial images representing the spectrogram of the voice signal. What is more, we showed that it is possible to perform a successful detection of Parkinson's disease using only frequency-based features. A spectrogram enables visual representation of frequencies spectrum of a signal. It allows to follow the frequencies changes of a signal in time.

Models of Care Delivery for Patients With Parkinson Disease Living in Rural Areas

The elderly who suffer from chronic conditions have an increasingly difficult time accessing health care in rural areas compared with their healthy counterparts who seek and utilize less specialty care. Parkinson disease affects approximately 0.3% to 5% of the elderly population. However, a large portion of that population has difficulty accessing health care. The purpose of this study was to obtain an understanding of the access to care issues for patients with Parkinson disease and review solutions to aid their provision of care. A review of the literature found several models of care available to improve access to care issues for patients with Parkinson disease.

A clinical decision support system for prediction of pregnancy outcome in pregnant women with systemic lupus erythematosus

OBJECTIVE

Pregnancy among systemic lupus erythematosus (SLE)-affected women is highly associated with poor obstetric outcomes. Predicting the risk of foetal outcome is essential for maximizing the success of pregnancy. This study aimed to develop a clinical decision support system (CDSS) to predict pregnancy outcomes among SLE-affected pregnant women.

METHODS

We performed a retrospective analysis of 149 pregnant women with SLE, who were followed at Shariati Hospital (104 pregnancies) and a specialized clinic (45 pregnancies) from 1982 to 2014. We selected significant features (p<0.10) using a binary logistic regression model performed in IBM SPSS (version 20). Afterward, we trained several artificial neural networks (multi-layer perceptron [MLP] and radial basis function [RBF]) to predict the pregnancy outcome. In order to evaluate and select the most effective network, we used the confusion matrix and the receiver operating characteristic (ROC) curve. We finally developed a CDSS based on the most accurate network. MATLAB 2013b software was applied to design the neural networks and develop the CDSS.

RESULTS

Initially, 45 potential variables were analysed by the binary logistic regression and 16 effective features were selected as the inputs of neural networks (P-value<0.1). The accuracy (90.9%), sensitivity (80.0%), and specificity (94.1%) of the test data for the MLP network were achieved. These measures for the RBF network were 71.4%, 53.3%, and 79.4%, respectively. Having applied a 10-fold cross-validation method, the accuracy for the networks showed 75.16% accuracy for RBF and 90.6% accuracy for MLP. Therefore, the MLP network was selected as the most accurate network for prediction of pregnancy outcome.

CONCLUSION

The developed CDSS based on the MLP network can help physicians to predict pregnancy outcomes in women with SLE.

Accuracy Improvement for Predicting Parkinson's Disease Progression

Parkinson's disease (PD) is a member of a larger group of neuromotor diseases marked by the progressive death of dopamineproducing cells in the brain. Providing computational tools for Parkinson disease using a set of data that contains medical information is very desirable for alleviating the symptoms that can help the amount of people who want to discover the risk of disease at an early stage. This paper proposes a new hybrid intelligent system for the prediction of PD progression using noise removal, clustering and prediction methods. Principal Component Analysis (PCA) and Expectation Maximization (EM) are respectively employed to address the multi-collinearity problems in the experimental datasets and clustering the data. We then apply Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for prediction of PD progression. Experimental results on public Parkinson's datasets show that the proposed method remarkably improves the accuracy of prediction of PD progression. The hybrid intelligent system can assist medical practitioners in the healthcare practice for early detection of Parkinson disease.

High-Accuracy Detection of Early Parkinson's Disease through Multimodal Features and Machine Learning

Early (or preclinical) diagnosis of Parkinson's disease (PD) is crucial for its early management as by the time manifestation of clinical symptoms occur, more than 60% of the dopaminergic neurons have already been lost. It is now established that there exists a premotor stage, before the start of these classic motor symptoms, characterized by a constellation of clinical features, mostly non-motor in nature such as Rapid Eye Movement (REM) sleep Behaviour Disorder (RBD) and olfactory loss. In this paper, we use the non-motor features of RBD and olfactory loss, along with other significant biomarkers such as Cerebrospinal fluid (CSF) measurements and dopaminergic imaging markers from 183 healthy normal and 401 early PD subjects, as obtained from the Parkinson's Progression Markers Initiative (PPMI) database, to classify early PD subjects from normal using Naïve Bayes, Support Vector Machine (SVM), Boosted Trees and Random Forests classifiers. We observe that SVM classifier gave the best performance (96.40% accuracy, 97.03% sensitivity, 95.01% specificity, and 98.88% area under ROC). We infer from the study that a combination of non-motor, CSF and imaging markers may aid in the preclinical diagnosis of PD.

Discriminating Between Patients With Parkinson's and Neurological Diseases Using Cepstral Analysis

In this paper, we wanted to discriminate between two groups of patients (patients who suffer from Parkinson's disease and patients who suffer from other neurological disorders). We collected a variety of voice samples from 50 subjects using different recording devices in different conditions. Subsequently, we analyzed and extracted features from these samples using three different Cepstral techniques; Mel frequency cepstral coefficients (MFCC), perceptual linear prediction (PLP), and ReAlitive SpecTrAl PLP (RASTA-PLP). For classification we used leave one subject out validation scheme along with five different supervised learning classifiers. The best obtained result was 90% using the first 11 coefficients of the PLP and linear SVM kernels.

Patient classification and outcome prediction in IgA nephropathy

OBJECTIVE

IgA Nephropathy (IgAN) is a common kidney disease which may entail renal failure, known as End Stage Kidney Disease (ESKD). One of the major difficulties dealing with this disease is to predict the time of the long-term prognosis for a patient at the time of diagnosis. In fact, the progression of IgAN to ESKD depends on an intricate interrelationship between clinical and laboratory findings. Therefore, the objective of this work has been the selection of the best data mining tool to build a model able to predict (I) if a patient with a biopsy proven IgAN will reach ESKD and (II) if a patient will reach the ESKD before or after 5 years.

MATERIAL AND METHODS

The largest available cohort study worldwide on IgAN has been used to design and compare several data-driven models. The complete dataset was composed of 1174 records collected from Italian, Norwegian, and Japanese IgAN patients, in the last 30 years. The data mining tools considered in this work were artificial neural networks (ANNs), neuro fuzzy systems (NFSs), support vector machines (SVMs), and decision trees (DTs). A 10-fold cross validation was used to evaluate unbiased performances for all the models.

RESULTS

An extensive model comparison based on accuracy, precision, recall, and f-measure was provided. Overall, the results indicate that ANNs can provide superior performance compared to the other models. The ANN for time-to-ESKD prediction is characterized by accuracy, precision, recall, and f-measure greater than 90%. The ANN for ESKD prediction has accuracy greater than 90% as well as precision, recall, and f-measure for the class of patients not reaching ESKD, while precision, recall, and f-measure for the class of patients reaching ESKD are slightly lower. The obtained model has been implemented in a Web-based decision support system (DSS).

CONCLUSIONS

The extraction of novel knowledge from clinical data and the definition of predictive models to support diagnosis, prognosis, and therapy is becoming an essential tool for researchers and clinical practitioners in medicine. The proposed comparative study of several data mining models for the outcome prediction in IgAN patients, using a large dataset of clinical records from three different countries, provides an insight into the relative prediction ability of the considered methods applied to such a disease.

ICT and the future of health care: aspects of health promotion

PURPOSE

Increasingly, Information and Communication Technology (ICT) applications enter the daily lives of consumers. Availability of various multimedia interfaces offers the opportunity to develop and adjust ICT solutions to all aspects of society including health care. To address the challenges of the ongoing adaptive progress of ICT, decision makers profit from estimates of expectable merits and risks of future technological developments. The aim of the present study was to assess the prevailing opinions and expectations among Austrian stakeholders regarding ICT-assisted health promotion.

METHODS

In total, 73 experts (74% males) engaged in the Austrian health care sector participated in a biphasic online Delphi survey. Panellists were assigned to three groups representing medical professionals, patient advocates, and administrative personnel. In a scenario-based questionnaire, experts evaluated potential advantages and barriers as well as degree of innovation, desirability, and estimated date of implementation of six future ICT scenarios. Scenario-specific and consolidated overall opinions were ranked. Inter-group differences were assessed using ANOVA.

RESULTS

Panellists expected the future ICT-supported health promotion strategies to especially improve the factors living standard (56%), quality of health care (53%), and patient's knowledge (44%). Nevertheless, monetary aspects (57%), acceptance by patient advocates (45%), and data security and privacy (27%) were considered as the three most substantial hampering factors for ICT applications. Although overall mean desirability of the scenarios was quite high (80%) amongst panellists, it was considerably lower in medical professionals compared to patient advocates and administrative personnel (p=0.006). This observation suggests a more precautious attitude of this specific interest group regarding technological innovations.

CONCLUSIONS

The present Delphi survey identified issues relevant for successful implementation of ICT-based health care solutions, providing a compilation of several areas that might require further research. In the light of ageing societies facing the perceived threat of permanent online surveillance, different requirements and expectations of end users should be accounted for by various stakeholders. Thus, close collaboration could facilitate the harmonization process on hot health topics among interest groups.

A new hybrid intelligent system for accurate detection of Parkinson's disease

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.