A NOVEL AND HYBRID WHALE OPTIMIZATION WITH RESTRICTED CROSSOVER AND MUTATION BASED FEATURE SELECTION METHOD FOR ANXIETY AND DEPRESSION

Introduction: Anxiety and depression are two leading human psychological disorders. In this work, several swarm intelligence- based metaheuristic techniques have been employed to find an optimal feature set for the diagnosis of these two human psychological disorders. Subjects and Methods: To diagnose depression and anxiety among people, a random dataset comprising 1128 instances and 46 attributes has been considered and examined. The dataset was collected and compiled manually by visiting the number of clinics situated in different cities of Haryana (one of the states of India). Afterwards, nine emerging meta-heuristic techniques (Genetic algorithm, binary Grey Wolf Optimizer, Ant Colony Optimization, Particle Swarm Optimization, Artificial Bee Colony, Firefly Algorithm, Dragonfly Algorithm, Bat Algorithm and Whale Optimization Algorithm) have been employed to find the optimal feature set used to diagnose depression and anxiety among humans. To avoid local optima and to maintain the balance between exploration and exploitation, a new hybrid feature selection technique called Restricted Crossover Mutation based Whale Optimization Algorithm (RCM-WOA) has been designed. Results: The swarm intelligence-based meta-heuristic algorithms have been applied to the datasets. The performance of these algorithms has been evaluated using different performance metrics such as accuracy, sensitivity, specificity, precision, recall, f-measure, error rate, execution time and convergence curve. The rate of accuracy reached utilizing the proposed method RCM-WOA is 91.4%. Conclusion: Depression and Anxiety are two critical psychological disorders that may lead to other chronic and life-threatening human disorders. The proposed algorithm (RCM-WOA) was found to be more suitable compared to the other state of art methods

Enhancing Feature Selection Accuracy using Butterfly and Lion Optimization Algorithm with Specific Reference to Psychiatric Disorder Detection & Diagnosis

As the complexity of medical computing increases the use of intelligent methods based on methods of soft computing also increases. During current decade this intelligent computing involves various meta-heuristic algorithms for Optimization. Many new meta-heuristic algorithms are proposed in last few years. The dimension of this data has also wide. Feature selection processes play an important role in these types of wide data. In intelligent computation feature selection is important phase after the pre-processing phase. The success of any model depends on how better optimization algorithms is used. Sometime single optimization algorithms are not enough in order to produce better result. In this paper meta-heuristic algorithm like butterfly optimization algorithm and enhanced lion optimization algorithm are used to show better accuracy in feature selection. The study focuses on nature based integrated meta-heuristic algorithm like Butterfly Optimization and lion-based optimization. Also, in this paper various other Optimization algorithms are analyzed. The study shows how integrated methods are useful to enhance the accuracy of any computing model to solve Complex problems. Here experimental result has shown by proposing and hybrid model for two major psychiatric disorders one is known as autism spectrum and second one is Parkinson's disease

A Data Mining Analysis Over Psychiatric Database for Mental Health Classification

Data mining approach help in various extraction unit from large dataset. Mental health and brain statistics is an important body part which is directly connected with the human body. There are many symptoms which can observe from the mental health care dataset and especially with psychiatric dataset. There are many health disease associated with such symptoms i.e. Anxiety, Mood disorder, Depression etc. Diseases such as mental retardation, Alzheimer, dementia and many other related with such symptoms. A proper classification and finding its efficiency is needed while dealing with different set of data. A classification of these disease and analysis requirement make it working for user understanding over disease. In this paper different classification algorithm is presented and classification is performed using J48 (C4.5), Random forest (RF) and Random Tree (RT) approach. The classification with precision, recall, ROC curve and F-measure is taken in as computation parameter. An analysis shows that the Random tree based approach find efficient result while comparing with J48 and Random forest algorithm

Knowledge Base for MENTAL AI, in Data Science Context

Globally, 1 in 7 people has some kind of mental or substance use disorder that affects their thinking, feelings, and behaviour in everyday life. Mental well-being is vital for physical health. No Health Without Mental Health! People with mental health disorders can carry on with normal life if they get the proper treatment and support. Mental disorders are complex to diagnose due to similar and common symptoms for numerous types of mental illnesses, with a minute difference among them. In the era of big, the challenge stays to make sense of the huge amount of health research and care data. Computational methods hold significant potential to enable superior patient stratification approaches to the established clinical practice, which in turn are a pre-requirement for the development of effective personalized medicine approaches. Personalized psychiatry also plays a vital role in predicting mental disorders and improving diagnosis and optimized treatment. The use of intelligent systems is expected to grow in the medical field, and it will continue to pose abundant opportunities for solutions that can help save patients’ lives. As it does for many industries, Artificial Intelligence (AI) systems can support mental health specialists in their jobs. Machine learning algorithms can be applied to find different patterns in the most diverse sets of data. This work aims to examine and compare different machine learning classification methodologies to predict different mental disorders and, from that, extract knowledge that can help mental health professionals in their tasks. Our algorithms were trained using a total dataset of 3353 patients from different hospital units. These data are divided into three subsets of data, mainly by the characteristics that the pathologies present. We evaluate the performance of the algorithms using different metrics. Among the metrics applied, we chose the F1 score to compare and analyze the algorithms, as it is the most suitable for the data we have since they found themselves imbalances. In the first evaluation, we trained our models, using all the patient’s symptoms and diagnoses. In the second evaluation, we trained our models, using only the symptoms that were somehow related to each other and that influenced the other pathologies.Milhões de pessoas em todo o mundo são afetadas por transtornos mentais que influenciam o seu pensamento, sentimento ou comportamento. A saúde mental é um pré-requisito essencial para a saúde física e geral. Pessoas com transtornos mentais geralmente precisam de tratamento e apoio adequados para levar uma vida normal. A saúde mental é uma condição de bem-estar em que um indivíduo reconhece as suas habilidades, pode lidar com as tensões quotidianas da vida, trabalhar de forma produtiva e pode contribuir para a sua comunidade. A saúde mental afeta a vida das pessoas com transtorno mental, as suas profissões e a produtividade da comunidade. Boa saúde mental e resiliência são essenciais para a nossa saúde biológica, conexões humanas, educação, trabalho e alcançar o nosso potencial. A pandemia do covid-19 impactou significativamente a saúde mental das pessoas, em particular grupos como saúde e outros trabalhadores da linha de frente, estudantes, pessoas que moram sozinhas e pessoas com condições de saúde mental pré-existentes. Além disso, os serviços para transtornos mentais, neurológicos e por uso de substâncias foram significativamente interrompidos. Os transtornos mentais são classificados como de diagnóstico complexo devido à semelhança dos sintomas. Consultas regulares de saúde de pessoas com transtornos mentais graves podem impedir a morte prematura. A dificuldade dos especialistas em diagnosticar é geralmente causada pela semelhança dos sintomas nos transtornos mentais, como por exemplo, transtorno de bordeline e bipolar. Os algoritmos de aprendizado de máquina podem ser aplicados para encontrar diferentes padrões nos mais diversos conjuntos de dados. Este trabalho, visa examinar e comparar diferentes metodologias de classificação de aprendizado de máquina para prever difentes transtornos mentais e disso, extrair conhecimento que possam auxiliar os profissionais da area de saude mental, nas suas tarefas. Os nossos algoritmos, foram treinados utilizando um conjunto total de dados de 3353 pacientes, provenientes de diferentes unidades hospitalares. Esses dados, estão repartidos em três subconjuntos de dados, principalmente, pelas características que as patologias apresentam. Avaliamos o desempenho dos algoritmos usando diferentes métricas. Dentre as métricas aplicadas, escolhemos o F1 score para comparar e analisar os algoritmos, pois é o mais adequado para os dados que possuímos. Visto que eles se encontravam desequilíbrios. Na primeira avaliação, treinamos os nossos modelos, utilizando todos os sintomas e diagnósticos dos pacientes. Na segunda avaliação, treinamos os nossos modelos, utilizando apenas os sintomas que apresentavam alguma relação entre si e que influenciavam nas outras patologias

A Review on Machine Learning Techniques for Neurological Disorders Estimation by Analyzing EEG Waves

With the fast improvement of neuroimaging data acquisition strategies, there has been a significant growth in learning neurological disorders among data mining and machine learning communities. Neurological disorders are the ones that impact the central nervous system (including the human brain) and also include over 600 disorders ranging from brain aneurysm to epilepsy. Every year, based on World Health Organization (WHO), neurological disorders affect much more than one billion people worldwide and count for up to seven million deaths. Hence, useful investigation of neurological disorders is actually of great value. The vast majority of datasets useful for diagnosis of neurological disorders like electroencephalogram (EEG) are actually complicated and poses challenges that are many for data mining and machine learning algorithms due to their increased dimensionality, non stationarity, and non linearity. Hence, an better feature representation is actually key to an effective suite of data mining and machine learning algorithms in the examination of neurological disorders. With this exploration, we use a well defined EEG dataset to train as well as test out models. A preprocessing stage is actually used to extend, arrange and manipulate the framework of free data sets to the needs of ours for better training and tests results. Several techniques are used by us to enhance system accuracy. This particular paper concentrates on dealing with above pointed out difficulties and appropriately analyzes different EEG signals that would in turn help us to boost the procedure of feature extraction and enhance the accuracy in classification. Along with acknowledging above issues, this particular paper proposes a framework that would be useful in determining man stress level and also as a result, differentiate a stressed or normal person/subject

Combining Artificial Intelligence with Traditional Chinese Medicine for Intelligent Health Management

The growth of artificial intelligence (AI) is being referred to as the beginning of "the fourth industrial revolution". With the rapid development of hardware, algorithms, and applications, AI not only provides a new concept and relevant solutions to solve the problem of complexity science but also provides a new concept and method to promote the development of traditional Chinese medicine (TCM). In this study, based on the research and development of AI technology applications in biomedical and clinical diagnosis and treatment, we introduce AI technologies in current TCM research. This can have applications in intelligent clinical information acquisition, intelligent clinical decision, and efficacy evaluation of TCM; intelligent classification management, intelligent prescription, and drug research in Chinese herbal medicine; and health management. Furthermore, we propose a framework of "intelligent TCM" and outline its development prospects