Early detection Parkinson’s Disease By Machine Learning Technique using Artificial Intelligence (AI) method, Author of this article is joint Researcher and, is a Master of Science in Computer Science, USA with the help of India’s one of the best Neuro physician doctor, who has been honored and awarded “PADMA SHRI” fourth-highest civilian award in the Republic of India.
Machine Learning Process: Machine learning concepts are the statistical models inspired by the functioning of human brain cells called neurons. Artificial Neural Network (ANN) can mathematically model how the biological brain works, allowing the machine to mimic the human brain.
Parkinson’s Disease Founder: James Parkinson FGS (11 April 1755 – 21 December 1824) was an English surgeon, apothecary, geologist, paleontologists was the first to describe neurological disorder called ‘paralysis agitation’ or ‘the shaking palsy’ in 1817.
Abstract: Parkinson’s Disease (PD) is considered a malison for mankind for several decades. Its detection with the help of an automated system is a subject undergoing intense study. This entails a need for incorporating a machine learning model for the PD.
For discovering a full proof model, the cardinal prerequisite is to study the existing computational intelligent techniques in the field of research used for Early detection Parkinson’s Disease. Many existing models focus on singular modality or have a cursory analysis of multiple modalities.
Early detection Parkinson’s Disease encouraged us to provide a comparative literature study of four main modalities signifying major symptoms used for early detection of PD, namely, tremor at rest, bradykinesia, rigidity, and, voice impairment. State- of-the-art machine learning implementations namely Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-nearest neighbors (KNN), Stochastic Gradient Descent (SGD) and Gaussian Naive Bayes (GNB) are executed in these modalities with their respective datasets.
Furthermore, ensemble approaches such as Random Forest Classifier (RF), Adaptive Boosting (AB) and Hard Voting (HV) are implemented. Our results are compared with those obtained with their respective research. Among all the tests, applying Random Forest (RF) on Static Spiral Test (for detecting tremor) gave us the most significant result in PD, i.e. the highest accuracy of 99.79%.
This leads to the conclusion that the multi- modal approach with the help of the ensemble method should be used to get better and accurate results. Hence taking the help for ML and AI for Early detection Parkinson’s Disease is greatness of science.
Parkinson’s has four main symptoms:
- Bradykinesia/ Slowness of movement
- Tremor in hands, arms, legs, jaw, or head
- Rigidity-Muscle stiffness, where muscle remains contracted for a long time
- Voice Impaired balance and coordination
Parkinson’s disease (PD) is a long-termed, neurological disorder that causes a person to lose control over several body functions including speech. It is the second most common neurodegenerative disease after Alzheimer’s disease. The loss of nerve cells in the part of the brain called the substantia nigra causes PD. These nerve cells or neurons create an organic chemical named dopamine which acts as a neurotransmitter between the parts of the brain and central nervous system that helps to control and co-ordinate body movements.
Although this disease can be diagnosed at an early stage. But with the advent of strong tools like Artificial Intelligence and Machine Learning, this took a subtle turn, various state-of-the-art machine learning tools and techniques analyzed the high dimensions of data in the datasets which made the work of prediction simple. Now it is highly necessary for Early detection Parkinson’s Disease and its remedy.
Bradykinesia: Bradykinesia or slowness of movement is one of the major symptoms of PD. It results from a failure of basal ganglia output to reinforce the cortical mechanisms that develop and execute the motor system commands which results in motion. The reduced dopaminergic substances to the striatum may result in increased neuronal firing which serves as a hindrance to the basal ganglia output. Due to this deficiency, the person starts to experience abnormal movement activities, which results in difficulty with self- paced motions.
Bradykinesia can be detected by various techniques and its analysis. Data Mining techniques and its applications are widely used to adhere to this cause. Along with it, a new technology ‘Leap Motion’ aimed at detecting bradykinesia with ease.
Tremor at rest: Tremor is a rhythmical and involuntary oscillatory movement of the body parts. It is a symptom diagnosed in various diseases. There are some peculiar characteristics by which Early detection Parkinson’s Disease affected tremors are identified. PD patients have a high effect of tremors in hands compared to other body parts.
One of the major diagnostic features is the suppression of rest tremor during movement initiation. These studies prove that tremor dominant PD is a distinct subtype that can help in the early detection of PD. Tremor at rest is itself a wide field of research and various techniques help in its detection.
Rigidity: It is one of the four major symptoms for the detection of Early detection Parkinson’s Disease. It refers to the abnormal stiffness in the limbs or other body parts, which prevents muscles from stretching or relaxing. It can occur to one or both sides of the body. Characteristics of Rigidity are stiffness in muscles, like facial muscles. Disability to display countenance and not able to enunciate fluently paved way for speech therapy.
It is treated with various medications like levodopa and various inhibitors, namely, catechol-O-methyltransferase (COMT) inhibitors, and monoamine oxidase-B (MAO-B) inhibitors. Physiotherapy is also administered to pacify the excruciating effects of the disease. As the disease progresses the day to day tasks become more difficult. Hence, specialists perform occupational therapy to ameliorate their effects.
Voice Impairment: To consider voice impairment in the early detection of PD or not is a debate going on for many years. Many renowned neurologists do not consider this symptom as an early sign of PD. Whereas, some adamantly quote that vocal symptoms are the most prominent ones at an early stage along with tremor and rigidity. An intermittent solution is provided in that voice impairment can be described at an Early detection Parkinson’s Disease and there is conclusive evidence of the late appearance of dysarthria in PD patients.
PD causes damage in the nerves which affects the central nervous system simultaneously deteriorating the substantia nigra pars compacta. This results in limiting the secretion of dopamine, which helps to produce smooth muscle movement hence affecting the facial muscles and the countenance of the affected person.
Early detection Parkinson’s Disease includes all the aspects such as biological, chemical and geneti Gradually some of them evolved by applying machine learning and artifi- cial intelligence, which contributed as a major tool for the early detection of this disease. PD is a diagnostic disease and there is no confirm symptom or guaranteed detection technique, there are many people who have given their best shot for the Early detection Parkinson’s Disease and prevention of this disease.
RESULTS AND DISCUSSION:
For Early detection Parkinson’s Disease, all the Machine learning algorithms are compared based on Specificity and Sensitivity. The ability to predict the prob- ability of actual positive trials and the ability to predict the probability of actual negative trails are denoted by the terms Sensitivity and Specificity respectively. These two terms are evident in all the results (Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6 and Fig. 7). Along with the algorithms, three ensemble approaches are implemented namely, RF (Bagging method), AB (Boosting method) and HV (Voting method) in all four modalities to increase the proposed accuracy.
Bradykinesia: To understand the symptoms and extract useful features from the data, various machine learning algorithms have been implemented. The dataset used is taken from the UCI repository. The results of all the algorithms are enlisted in Fig 2. The highest accuracy of 97.5% is obtained by implementing RF and HV. First sign for Early detection Parkinson’s Disease.
Tremor at rest: Various machine learning models shown in the figure are applied and many of them resulted in higher accuracy com- pared to previous studies and have used the dataset from the UCI repository. The highest accuracy of 99.79%, 99.76% and 99.71% was obtained by RF in SST, DST, and STCP respectively as shown in Fig. 3, Fig. 4, and Fig. 5. helps clearly for Early detection Parkinson’s Disease.
Rigidity: To understand the significance of gait analysis for rigidity detection, Physio net’s Gait Database is implemented. Pre-processed the data, extracted the features and implemented various state-of-the-art machine learning algorithms. The highest accuracy was noted to be 83.12% in Random Forest Classifier and Adaptive Boosting as shown in Fig. 6. Sign of Early detection Parkinson’s Disease.
Voice Impairment: Major roll for Early detection Parkinson’s Disease. The dataset of the UCI repository is used which consists the data of 31 people (23 PD affected patients and 8 healthy subjects). Various machine learning algorithms are implemented for the classification of the disease and the results are shown in fig. 7, got a maximum accuracy of 97.96% by implementing KNN on the same dataset.