Description:Field of the Invention
This invention refers to medical diagnosis and computational intelligence for the early diagnosis of Parkinson's disease (PD). It focuses on developing an AI-platform based on integrating Random Forest for gait analysis, Light Gradient Boosting Machine for voice data analysis, and a multimodal Light Gradient Boosting Machine model to fuse both data modalities for enhanced diagnostic accuracy. By taking advantage of ensemble learning strategies, the system provides early detection and accurate prediction of Parkinson's disease. The method combines multimodal data with sophisticated machine learning methods to identify minor symptoms of Parkinson's disease. The method provides better accuracy in prediction, enables earlier diagnosis, and offers options for customizing treatment plans. This invention exhibits significant improvements over conventional single-modality-based diagnostic systems.

Objective of this Invention

The invention aims to develop a simple, AI-based tool for early Parkinson's disease detection using gait and voice patterns. Because Parkinson's impacts movement and speech, our system employs Machine Learning—Random Forest for gait and Light Gradient Boosting for voice—to identify minor alterations that could signify the disease. We increase the accuracy of diagnostics by integrating these insights and deliver users a report in detail. If Parkinson's is identified, we refer users to the top hospitals for treatment. Our goal is to equip both patients and medical practitioners with an early warning system for timely intervention and improved care.
Background of the Invention

Parkinson's disease is a neurodegenerative condition that largely targets movement but also involves non-motor symptoms like cognitive impairment, mood changes, and autonomic dysfunction. Traditional diagnostic approaches, which are based on clinical assessment and imaging modalities such as MRI, are subjective and tend to identify the disease only when it is well established. Such a delay restricts early treatment and individualized interventions that might retard disease progression. The invention fills these gaps through the use of an AI system that incorporates Random Forest for analyzing gait, Light Gradient Boosting Machine for analyzing voice data, and multimodal Light Gradient Boosting Machine model, in a non-invasive, stable, and reliable method of Parkinson's disease diagnosis at its onset.

US20240000369A1 AI-Powered Parkinson's Disease Detection. This patent is basically an AI based system that aims to enhance the early detection of PD through the use of machine learning algorithms to enhance the accuracy of diagnosis. It focuses on diagnostic accuracy via data-driven models that reflect a set of biometric and clinical indicators. These technologies incorporate AI with multi-modal data, such as features from gait, speech, and handwriting, with the view of providing non-invasive and more effective diagnosis. The system can identify PD early on as it monitors patterns in data, thus permitting for prompt intervention or further management of the disease progression.

US11375945B2 Machine Learning for Parkinson's Diagnosis. This patent focuses on trying ML models for application in the diagnosis of Parkinson's disease. It uses predictive models based on gait data and voice features to detect Parkinson's. SVMs and random forests are used to differentiate between healthy individuals and those with Parkinson's disease. The novelty behind the system is that it can analyze multitudinous data points collected from real-time monitoring equipment. In this way, one has a more efficient and not expensive method of diagnosis, which enhances diagnostic accuracy in clinical settings.

US11013452B1 Parkinson's Disease Diagnosing Apparatus by AI. This is an AI-based diagnostic apparatus utilizing multimodal data analysis that aims at diagnosing Parkinson's disease. It takes the combination of multiple sensor inputs like motion sensors, voice records, and biometric inputs to analyze the diagnostic aspect of the disease. It relies on deep learning based pattern anomaly detection in gait and speech to arrive at its diagnoses. The system seeks to reduce the time interval to diagnosis and improve the accuracy of detection of Parkinson's disease even at early stages when symptoms are subtle.

WO2022115777A2 Parkinson's Prediction with Machine Learning. This patent examines the use of machine learning to predict the time to onset and progression of Parkinson's disease. The system works based on the combination of clinical data, genetic factor input, and patient history in training predictive models so that the chances of patients developing Parkinson's can be estimated. It applies neural networks and supervised learning methods for the purposes of predicting early symptoms and thus prompting earlier preventive care by the healthcare providers. This predictive model gives clinicians tools to assess and monitor long-term disease progression, thus making it easier to manage patient treatment effectively.

KR102097738B1 AI-Powered Parkinson's Diagnosis Device. This patent introduces an AI-based device for the diagnosis of Parkinson's disease. The system analyzes several sensor data, including gait measurements, voice characteristics, and facial expressions, with advanced AI algorithms. With the help of machine learning techniques, it identifies the patterns that are indicative of Parkinson's and gives a diagnostic recommendation. The aim of the device is to give personalized diagnosis so that health care providers can detect Parkinson's disease earlier and more accurately than through traditional methods. The invention is suited to use in the clinics and at home, allowing for continuous monitoring to be made on patients for timely interventions.

EP4420139A1 The patent is on AI-powered diagnosis systems for Parkinson's disease. It integrates the application of neuroimaging methods, including MRI and PET scans, with AI algorithms for determining patterns of brain activity that correlate with Parkinson's disease. It incorporates both genetic and clinical data to provide an overall early detection system of risk and progression of diseases. The invention points to the importance of personalizing the treatment procedure by using big data analytics to improve the detection and management of Parkinson's disease.

KR20240012733A Multimodal Parkinson's Prediction AI System. The new patent will bring an AI multimodal system capable of predicting a patient with Parkinson's disease; this includes analysis on voice, genetic data, and other gait data streams combined through advanced fusion techniques, thereby improving their accuracy of prediction. Across the multiple modes of input, such patterns would be learned from and further integrated into unified models that make predictions from them. This invention propounds the idea of giving a holistic view of Parkinson's disease, as it enables early detection and personalized patient monitoring over a period.

Summary of the Invention

The invention proposes a new AI-based diagnostic system that employs state-of-the-art machine learning algorithms to diagnose Parkinson's disease at an early stage and predict its course. Leveraging Random Forest for gait analysis and Light Gradient Boosting Machine for voice analysis, the system detects significant patterns in patient data. In multimodal fusion, a Light Gradient Boosting Machine model fuses gait and voice features with enhanced diagnostic performance. Such a multi-modal strategy allows for accurate predictions and produces real-time diagnostic results, providing information on disease progression, personalized treatment recommendations, and early specialist referral.

Gait and Voice Data Processing: The system processes gait and voice data by extracting spatiotemporal features from the gait, i.e., stride length, cadence, and velocity, and acoustic features from the voice, i.e., jitter, shimmer, and Mel-Frequency Cepstrum Coefficients (MFCCs). These features so extracted are then processed by a Random Forest model for gait assessment and by a Light Gradient Boosting Machine (LightGBM) for voice evaluation, thus making the diagnostic framework robust. For enhanced detection accuracy, attention mechanisms assign higher importance to the most influential attributes, which allows the model to focus on salient patterns that are Parkinson's disease-relevant. This multimodal approach not only improves early detection but also allows discrimination between healthy individuals and mildly motor-impaired individuals with accuracy, forming the foundation for timely intervention and personalized disease management.

Multimodal Integration and Clustering of Disease Progression: Utilizing a multimodal Light Gradient Boosting Machine (LightGBM) model, the system combines gait and vocal data to optimize diagnostic accuracy and classify patients into disease progression groups. The combination of motor and vocal biomarkers allows for accurate monitoring, leading to early identification and personalized treatment plans. Innovative clustering methods cluster patients into individual progression phases, maximizing therapeutic intervention and enhancing long-term disease control. With continual improvement in forecast with real patients' information, the model evolves its predicting performance, leading to proactive health outcomes that modify its approach with regards to Parkinson's disease changing aspects.

Brief Description of Drawings

The innovation will be depicted in detail with the reference to the model epitomes appeared within the figures wherein:
Figure-1: Workflow of Classification Model.
Figure-2: Flowgorithm representing the work flow of Parkinson’s Prediction Model.

Detailed Description of the Invention

Data Collection: In this system, data is gathered with multi-modalities to construct detailed datasets about the patient's condition. For instance, gait can be captured with wearable technology, like smartwatches, or motion capture systems tracking walking patterns. Stride length, cadence, and velocity are parameters analyzed. Voice data is captured with audio devices in controlled settings, recording acoustic characteristics such as jitter (pitch changes), shimmer (amplitude change), and Mel-Frequency Cepstrum Coefficients (MFCCs). Voice data is important for early diagnosis and monitoring of Parkinson's disease.

Preprocessing: The collected data is subjected to a thorough preprocessing process to remove the maximum amount of noise and be ready for analysis. The process begins with noise reduction techniques to remove unwanted signals and artifacts and clean the data. Normalization then comes in to control measurements from varied devices and working conditions to achieve consistency and reduce variability. Feature extraction is thereafter performed, identifying significant features such as gait velocity, cadence, stride length, jitter, shimmer, and Mel-Frequency Cepstrum Coefficients (MFCCs), which act as major pointers in the assessment of Parkinson's disease. Via raw data conversion to meaningful and structured features, preprocessing plays an essential role in enabling accurate analysis, model optimization, and diagnosis prediction reliability improvement.

Gait Analysis: Gait analysis is employed to detect early-stage Parkinson's disease by examining how a person walks. According to a Random Forest model, the system compares significant parameters such as stride length, cadence, and velocity and senses even the smallest differences in motor control. These small abnormalities, nevertheless, might go undetected on a day-to-day basis but can be early indicators of Parkinson's disease. By applying machine learning, the system is more objective and precise in its evaluation, and offers physicians and patients valuable insights for early diagnosis. People can implement treatment strategies that retard disease progression, improve mobility, and enjoy a better quality of life through timely interventions.

Voice Analysis: Voice analysis is an integral part of the diagnostic system, and it assists in picking up subtle changes in speech that can indicate Parkinson's disease. Normal-sounding conversations every day might pass undetected by the naked ear, but the system listens intently, breaking down crucial features like jitter, shimmer, and Mel-Frequency Cepstrum Coefficients (MFCCs) with the help of a Light Gradient Boosting Machine (LightGBM). These characteristics expose speech disabilities such as monotonicity, tremors, or softness—frequent but frequently neglected symptoms of the disease. By detecting these initial vocal alterations, the system offers a more complete and precise diagnosis, allowing individuals and medical professionals to take proactive measures in controlling the condition and enhancing quality of life.

Multimodal integration converges the results from both voice and gait analysis to get a more accurate understanding of someone's health. Although gait analysis identifies motor control problems in tiny increments, voice analysis finds impairments to speech—both important markers of Parkinson's disease. By integrating these outputs using a multimodal Light Gradient Boosting Machine (LightGBM) model, the system is able to detect complex patterns that may go undetected when each modality is independently analyzed. With this integrated strategy, diagnostic precision is improved with no important symptom left behind. It is more than processing data; this integration offers patients and doctors a more precise, trustworthy assessment, allowing earlier intervention that can enhance mobility, communication, and quality of life.

Diagnostic Results: Following a complete analysis of gait and voice information, the system produces a detailed diagnostic report, providing patients and healthcare professionals with clarity and direction. If Parkinson's disease symptoms are found, the report is more than a diagnosis—it is a tailored report with personalized advice, such as possible medication, specialist referrals, and rehabilitation approaches appropriate to the severity of the condition. This ensures timely intervention is provided to patients that may aid in the control of symptoms and enhancement of quality of life. In case of no symptoms detected, the system reassures the individual with a comprehensive analysis of its results, allaying fears while encouraging active monitoring of health. Through provision of understandable, actionable results, the system empowers individuals to manage their own health, allowing for intelligent dialogue with physicians and enabling early, effective treatment options when required.

Technical Workflow: The platform functions with a smooth and organized workflow, providing a seamless transition from raw data capture to interpretable diagnostic insights. It starts by capturing gait and voice recordings, catching minute but important indicators of Parkinson's disease. The data is thoroughly preprocessed prior to analysis—noise is minimized, measurements are normalized, and essential features such as stride length, cadence, jitter, and MFCCs are extracted for improved accuracy. The system then processes gait through a Random Forest model and voice through a Light Gradient Boosting Machine (LightGBM), both of which were trained to find early signs of impairment. These results are then combined in a multimodal LightGBM model so that the entire assessment can look at both motor and speech symptoms. Lastly, the system produces a comprehensive diagnostic report, providing patients and healthcare professionals with important insights as well as personalized recommendations for treatment or ongoing monitoring. Through the automation of this intricate process with accuracy and attention to detail, the system not only enhances early detection but also enables people to take proactive action in controlling their health.

User Interface and Accessibility: The platform offers a user-friendly interface for uploading gait and voice data via a web or mobile application. User-centered, it supports users with different levels of technical knowledge. After processing the data, the system produces clear, comprehensive reports with actionable advice for clinicians and patients.
Continual Learning and Adaptation: The system improves continuously with time as it is trained on new patient datasets. Continual learning maintains diagnostic accuracy aligned with varying patient populations and points in the progression of Parkinson's disease. Every new dataset increases the system's capability for early symptom detection and disease progression monitoring, giving it effective early detection and continuous monitoring abilities.

Advantages of Parkinson’s Predictor: AI-Powered Early Detection

One of the greatest strengths of our suggested model is its capability to identify Parkinson's disease in an early stage through gait and voice pattern analysis. Conventional diagnosis usually relies on observable symptoms, which tend to occur in later stages only. Our system utilizes Machine Learning algorithms—Random Forest for gait and Light Gradient Boosting for voice—to recognize slight aberrations before clinical detection. This early identification can go a long way towards enhancing treatment outcomes, enabling patients to receive medical attention earlier. The model also does away with the need for invasive tests, providing an easy and non-intrusive method that can be accessed remotely.

Another significant advantage is the holistic and actionable intelligence our system offers. Rather than merely identifying the disease, our platform produces elaborate reports that assist users in comprehending their illness. In the event of detection of Parkinson's, the system suggests the best hospitals to visit, guaranteeing prompt access to expert treatment. This capability fills the gap between diagnosis and treatment, enhancing healthcare efficiency. In addition, if there is no indication of Parkinson's, users are given reassurance and peace of mind. Through combining AI predictions with healthcare advice, our model promotes accessibility, precision, and overall patient experience.

Equivalents

The invention has flexibility in terms of using a variety of different methods to meet the same purpose—an early and accurate diagnosis of Parkinson's disease. While today's system takes advantage of the use of proprietary sensor technologies in order to pick up gait and voice characteristics, other types of sensors can be utilized for greater accessibility and flexibility in environments. In the same vein, although Random Forest and Light Gradient Boosting Machine(LightGBM) work well for gait and voice analysis, other machine learning models like deep learning architectures may improve predictive accuracy. Multimodal integration may also change, with various methods like hierarchical clustering or Gaussian mixture models improving how patients are clustered according to disease progression. Technological advances in attention mechanisms, i.e., self-attention or transformer models, would potentially enhance prioritization of features further to make detection even more accurate. Besides gait and voice, inclusion of other data sources such as facial expressions or brain scans may offer a yet more nuanced picture of the disease. With its embrace of advances in technology and machine learning, this invention is likely to become something even more incredible, consistently expanding diagnostic capability and providing patients more precise, customized, and prompt care. , Claims:Claims:
1. An AI-powered diagnostic system for Parkinson’s disease detection:
a) A Gait Analysis Module uses Random Forest to analyze key patterns of gait, including stride length, cadence, and velocity. This approach provides robust early detection of Parkinson's disease.
b) A Voice Analysis Module uses Light Gradient Boosting Machine to identify acoustic features such as jitter, shimmer, and MFCCs, detecting vocal impairments due to tremors or low speech volume.
c) A MultimodelIntegration Module uses Light Gradient Boosting Machine to combine outputs from gait and voice analyses, improving prediction accuracy and providing a holistic diagnosis of Parkinson's disease.
2. According to claim 1, the system emphasizes major temporal and spatial features from gait and voice data, highlighting the most significant patterns for the accurate detection of Parkinson's disease. Temporal features are variations in gait or speech, whereas spatial features refer to specific physical attributes, such as stride length or speech pitch.
3. As per claim 1, the system is capable of generating extensive reports on disease existence, the stage of disease progression, proposed treatments, and referrals to other specialists. These outputs help clinicians in making diagnoses, treatment planning, and patient care by providing insights personalized to gait and voice analysis.