Machine Learning Approaches for Detection and Diagnosis of Parkinson’s Disease - A Review

Parkinson's disease (PD) is a chronic neurological condition that is growing in prevalence and manifests both motor and non-motor symptoms. Most PD patients have trouble speaking, writing, and walking during the early stages of the disease. Analysis of speech problems has been effective in identifying Parkinson's disease. According to studies, 90% of Parkinson's disease patients experience speech problems. Even though there is no known cure for Parkinson's disease, using the right medication at an early stage can greatly reduce the symptoms. One of the key categorization issues for the diagnosis of Parkinson's disease is the correct interpretation of speech signals. The major goal of this project is to use deep learning and machine learning approaches to predict and categorize PD patients at an early stage. A trustworthy dataset from the UCI repository for Parkinson disease has been used to evaluate the method's performance. Several classification models are successfully used in this study for classification tasks, including Extreme Gradient Boosting (XGBoost), Ada Boost, Light Gradient Boosting Machine, CatBoost, Gradient Boosting, Random Forest, Ridge, Decision Tree, Logistic Regression, K Neighbors, SVM - Linear Kernel, Naive Bayes, and deep neural networks (DNN1, DNN2, DNN3). The Extreme Gradient Boosting classifier achieved the greatest classification accuracy of 92.18% (among the machine learning classifiers). By using the chosen features as input, the three layer deep neural network (DNN2) has the best accuracy of 95.41% amongst deep learning techniques. The collected results indicate that deep neural networks performed better than machine learning methods.

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

Chapter 1 - Early detection of neurological diseases using machine learning and deep learning techniques: A review

Millions of people worldwide suffer from Parkinson’s disease (PD), a neurodegenerative disorder marked by motor symptoms such as tremors, bradykinesia, and stiffness. Accurate early diagnosis is crucial for effective management and treatment. This article presents a novel review of Machine Learning (ML) and Deep Learning (DL) techniques for PD detection and progression monitoring, offering new perspectives by integrating diverse data sources. We examine the public datasets recently used in studies, including audio recordings, gait analysis, and medical imaging. We discuss the preprocessing methods applied, the state-of-the-art models utilized, and their performance. Our evaluation included different algorithms such as support vector machines (SVM), random forests (RF), convolutional neural networks (CNN). These algorithms have shown promising results in PD diagnosis with accuracy rates exceeding 99% in some studies combining data sources. Our analysis particularly showcases the effectiveness of audio analysis in early symptom detection and gait analysis, including the Unified Parkinson’s Disease Rating Scale (UPDRS), in monitoring disease progression. Medical imaging, enhanced by DL techniques, has improved the identification of PD. The application of ML and DL in PD research offers significant potential for improving diagnostic accuracy. However, challenges like the need for large and diverse datasets, data privacy concerns, and data quality in healthcare remain. Additionally, developing explainable AI is crucial to ensure that clinicians can trust and understand ML and DL models. Our review highlights these key challenges that must be addressed to enhance the robustness and applicability of AI models in PD diagnosis, setting the groundwork for future research to overcome these obstacles.

Machine Learning is a sub-category of Artificial Intelligence enabling computers with the ability of pattern recognition, or to continuously learn from, making predictions based on data, and carry out decisions without being specifically programmed for doing so. In this context, Machine Learning is a broader category of algorithms being able to use datasets to identify patterns, discover insights, and enhance understanding and make decisions or predictions. Compared with Machine Learning, Deep Learning is a particular branch of Machine Learning that makes use of Machine Learning functionality, and moves beyond its capabilities. Deep Learning Algorithm is interpreted as a layered structure that tries to replicate the structure of the human brain. These capabilities enable Machine Learning and Deep Learning Algorithms usage in applications to identify and respond to cybercriminals manifold cyberattacks. This is achieved by analyzing Big Datasets of cybersecurity incidents to identify patterns of malicious activities. For this purpose, Machine Learning and Deep Learning compare known threat event attacks with detected threat event attacks to identify similarities they automatically dealt with trained Machine Learning or Deep Learning model for response. Against this background, this chapter seeks to offer a clear explanation of the classification of Machine Learning and Deep Learning and comparing them with regard to effectivity and efficiency in their specific application domains. This requires (i) discussing the methodological background of Machine Learning and Deep Learning; (ii) introducing relevant application areas of Machine Learning and Deep Learning like Intrusion Detection Systems; and (iii) use cases showing how to combat against threat event attacks based cybersecurity risks. In this context, this chapter provides, in Sect. 8.1, a brief introduction in classical Machine Learning, which consists of Supervised, Unsupervised, and Reinforcement Machine Learning. In this regard, Sect. 8.1.1.1 introduces Supervised Machine Learning, while Sect. 8.1.1.2 refers to Unsupervised Machine Learning, and Sect. 8.1.1.3 focuses on Reinforcement Machine Learning. Sect. 8.1.1.4 finally compares the different Machine Learning methods with regard to advantages and disadvantages. Based on this methodological introduction of classical Machine Learning, Sect. 8.2.1 introduces in Machine Learning and cybersecurity issues. Machine Learning-based intrusion detection in industrial application is therefore the topic of Sect. 8.2.1.1. Section 8.2.1.2 introduces Machine Learning-based intrusion detection based on feature learning, and Machine Learning-based intrusion detection of unknown cyberattacks is the topic of Sect. 8.2.1.3. In Section 8.3, the classification of Deep Learning methods is given which contains in Sect. 8.3.1 the topics Feedforward Deep Neural Networks, Convolutional Feedforward Deep Neural Networks, Recurrent Deep Neural Networks, Deep Beliefs Networks, and the Deep Bayesian Neural Network. Based on this methodological background of Deep Learning methods, Sect. 8.3.2 introduces Deep Bayesian Neural Networks, while Sect. 8.3.3 refers to Deep Learning-based intrusion detection. Finally, Sect. 8.4 refers to Deep Learning methods in cybersecurity applications. Section 8.5 contains comprehensive questions from the topics Machine Learning and Deep Learning, followed by “References” with references for further reading.

Deep dual-side learning ensemble model for Parkinson speech recognition

Parkinson’s disease (PD) is a neurological progressive disorder and is most common among people who are above 60 years old. It affects the brain nerve cells due to the deficiency of dopamine secretion. Dopamine acts as a neurotransmitter and helps in the movement of the body parts. Once brain cells/neurons start dying due to aging, then it will lead to a decrease in dopamine levels. The symptoms of Parkinson’s are difficultly in doing regular/habitual movements, uncontrollable shaking of hands and limbs may encounter memory loss, stiff muscles, sudden temporary loss of control, etc. The severity of the disease will be worse if not diagnosed and treated at the early stages. This paper concentrates on developing Parkinson’s disease diagnosing system using machine learning techniques and algorithms. Machine Learning is an integral part of artificial intelligence it takes huge data as input and train by making use of existing algorithms to understand the pattern of the data. Based on the recognized pattern, the machine will act accordingly without any human intervention. In this work, two major approaches have been employed to diagnose PD. Initially, 26 vocal data of PD affected and healthy individual datasets are obtained from the UCI Machine Learning data repository, are taken as initial raw data/features. In pre-processing, the mRMR feature selection algorithm is employed to minimize the feature count and increase the accuracy rate. The selected features will further be extracted using the Stacked Autoencoder technique to improve and increase the accuracy rate and quality of classification with reduced run time. K-fold cross-validation is used to evaluate the predictive capability of the model and the effectiveness of the extracted features. Artificial Immune Recognition System – Parallel (AIRS-P), an immune inspired algorithm is employed to classify the data from the extracted features. The proposed system attained 97% accuracy, outperforms the benchmarked algorithms and proved its significance on PD classification.

The accurate diagnosis of Parkinson disease specifically in its initial stages is extremely complex and time consuming. Thus the accurate and efficient diagnosis of Parkinson disease has been a significant challenge for medical experts and researchers. In order to tackle the accurate diagnosis of Parkinson disease issue we proposed machine learning and deep neural networks based non-invasive prediction system for accurately and on time diagnosis of Parkinson disease. In the development of the system machine learning predictive models such as support vector machine, logistic regression and deep neural network were used for people with Parkinson disease and healthy people classification. The data set was splits into 70% for training purpose and 30% for testing. Furthermore, performance evaluation metrics such as classification accuracy, sensitivity, specificity and Matthews's correlation coefficient were utilized for model performance evaluation. The Parkinson disease dataset of 23 attributes and 195 instances available on UCI machine learning repository was used for testing of the proposed system. Through our experimental results analysis shows that the proposed system classified the Parkinson disease and healthy people effectively. We also investigated that deep neural performance of classification was excellent as compared to traditional machines learning classifiers. These finding suggest that the proposed diagnosis system could be used to accurately predict Parkinson disease.

Parkinson's disease (PD) is a globally significant health challenge, necessitating accurate and timely diagnostic methods to facilitate effective treatment and intervention. In recent years, self-supervised deep representation pattern learning (SS-DRPL) has emerged as a promising approach for extracting valuable representations from data, offering the potential to enhance the efficiency of voice-based PD detection. This research study focuses on investigating the utilization of SS-DRPL in conjunction with deep learning algorithms for voice-based PD classification. This study encompasses a comprehensive evaluation aimed at assessing the accuracy of various predictive models, particularly deep learning methods when combined with SS-DRPL. Two deep learning architectures, namely hybrid Long Short-Term Memory and Recurrent Neural Networks (LSTM-RNN) and Deep Neural Networks (DNN), are employed and compared in terms of their ability to detect voice-based PD cases accurately. Additionally, several traditional machine learning models are also included to establish a baseline for comparison. The findings of the study reveal that the incorporation of SS-DRPL leads to improved model performance across all experimental setups. Notably, the LSTM-RNN architecture augmented with SS-DRPL achieves the highest F1-score of 0.94, indicating its superior ability to detect PD cases using voice-based data effectively. This outcome underscores the efficacy of SS-DRPL in enabling deep learning models to learn intricate patterns and correlations within the data, thereby facilitating more accurate PD classification.

Focus issue: Artificial intelligence in medical physics.

Parkinson's Disease (PD) poses a significant risk to clinical experts due to its rapid progression, adversely affecting individuals afflicted by the condition. This neurological disorder manifests through a spectrum of motor and non-motor symptoms, ranging from motor impairments like stiffness and bradykinesia to mental health issues and stress-related illnesses. Early and accurate diagnosis is pivotal for effective treatment, yet traditional diagnostic methods present challenges in detection and management. Leveraging Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs), holds promise for enhancing PD diagnosis. This study provides an extensive literature review of recent advancements in DL-based PD diagnosis, encompassing various approaches and developments. Specifically, the proposed modification to the VGG19 model, incorporating additional layers and regularization techniques, demonstrates remarkable performance in distinguishing PD patients from healthy individuals. Experimental results indicate a 98% accuracy rate, underscoring the model's potential as a reliable tool for early PD detection. Moreover, the study evaluates performance metrics, including accuracy, precision, recall, and F1-score, across different DL models, with the modified VGG19 model outperforming alternatives. The findings suggest that DL algorithms, particularly the proposed model, exhibit superior capability in identifying relevant cases and minimizing misclassifications. In conclusion, this research presents a novel DL approach for PD diagnosis using MRI images, offering substantial advancements in accuracy and efficiency. The model's ability to detect PD with high precision non-invasively underscores its significance in facilitating timely treatment and improving patient outcomes. Ultimately, early and accurate diagnosis facilitated by DL methodologies has the potential to revolutionize PD management, marking a significant stride towards enhancing patient care in neurology.

WITHDRAWN: A benchmarking study of Parkinson's disease classification base on speech symptom features

Parkinson's disease (PD) and Parkinson plus syndromes (PPS) are neurodegenerative movement disorders caused by loss of dopamine in the basal ganglia. The diagnosis of both PD and PPS is complex as it is made solely on the basis of clinical features, with no established imaging modality to aid in the diagnosis. Technetium-99m-labeled tropane derivative (Tc-TRODAT-1) binds to the dopamine transporters present in the presynaptic membrane of the dopaminergic nerve terminal. The aim of this prospective study was to investigate the potential usefulness of Tc-TRODAT-1 imaging in the diagnosis of PD and PPS. Fifty-eight patients with a clinical diagnosis of idiopathic PD or PPS were recruited. The severity of the disease was assessed using the Hoehn and Yahr scale. Patients in stage I and II were considered as cases of Early PD. Twenty-five apparently healthy volunteers served as controls. Brain single-photon emission computed tomography/computed tomography in all the participants was performed 3-4 h after an injection of Tc-TRODAT-1. Specific uptake ratios (SURs) of striatum were calculated for both the left and right striatum, and the values were compared between PD, PPS, and healthy volunteers. A significant lower uptake of tracer activity was found in either of the striatum in PD and PPS cases compared with the control group, which showed a symmetrical comma-shaped striatal uptake. This was also reflected in the SUR values, which were significantly higher in the control group in comparison with the PD and PPS patients (P<0.001). A significant difference was also found in the SUR values between the cases of early PD and control group (P<0.001).No significant difference was noted among the SUR values in different Hoehn and Yahr stages. For clinical practice, both the visual analysis and the quantitative parameters of Tc-TRODAT-1 single-photon emission computed tomography/computed tomography showed usefulness in distinguishing cases of PD and PPS from the healthy individuals.

Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN

Parkinson’s disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. Over the past two decades, the use of wearable devices in combination with machine learning algorithms has provided promising methods for more objective and continuous monitoring of PD. Recent advances in artificial intelligence have provided new methods and algorithms for data analysis, such as deep learning (DL). The aim of this article is to provide a comprehensive review of current applications where DL algorithms are employed for the assessment of motor and non-motor manifestations (NMM) using data collected via wearable sensors. This paper provides the reader with a summary of the current applications of DL and wearable devices for the diagnosis, prognosis, and monitoring of PD, in the hope of improving the adoption, applicability, and impact of both technologies as support tools. Following PRISMA (Systematic Reviews and Meta-Analyses) guidelines, sixty-nine studies were selected and analyzed. For each study, information on sample size, sensor configuration, DL approaches, validation methods and results according to the specific symptom under study were extracted and summarized. Furthermore, quality assessment was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) method. The majority of studies (74%) were published within the last three years, demonstrating the increasing focus on wearable technology and DL approaches for PD assessment. However, most papers focused on monitoring (59%) and computer-assisted diagnosis (37%), while few papers attempted to predict treatment response. Motor symptoms (86%) were treated much more frequently than NMM (14%). Inertial sensors were the most commonly used technology, followed by force sensors and microphones. Finally, convolutional neural networks (52%) were preferred to other DL approaches, while extracted features (38%) and raw data (37%) were similarly used as input for DL models. The results of this review highlight several challenges related to the use of wearable technology and DL methods in the assessment of PD, despite the advantages this technology could bring in the development and implementation of automated systems for PD assessment.