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Ayurvedic Diagnosis using Machine Learning Techniques to examine the diseases by extracting the data stored in AyurDataMart
... Manjula. H.M et al. [14] focus on the application of machine learning techniques for Ayurvedic diagnosis. The study aims to leverage the data stored in AyurDataMart, a repository of Ayurvedic medical information, to develop a ML based diagnostic system. ...
Ayurvedic herbs hold immense importance in traditional Ayurvedic medicine. These herbs are considered vital elements in maintaining physical, mental, and spiritual balance in the human body. According to ayurveda, diseases arise due to imbalances in doshas namely Vata, Pitta, and Kapha. Ayurvedic practitioners diagnose diseases by analyzing the dosha imbalances in an individual’s body and mind. Ayurvedic herbs play a crucial role in balancing the doshas and promoting overall health and well-being. Each herb possesses specific properties that can either pacify or aggravate the three doshas.This research employs machine learning models to classify Ayurvedic herbs according to their dosha balancing properties. Using a 90-10 split, the data was randomly split into a training set and a test set. Data preprocessing using random oversampling and artificial sample generation using CTGAN is also performed on the dataset. A total of six experiments are performed on the dataset. Various classification models like SVM, KNN, RF, DT and XGBoost were used for classification. Using four target classes without oversampling, the XGBoost classifier yielded the best classification models with an impressive accuracy of 96%, Precision of 97%, Recall of 96%, F1-score of 96% and Root mean square error of 0.16. The experimental results provide compelling evidence that the proposed model, which uses ensemble learning methods, significantly outperforms conventional methods.
... Knowledge-based Expert Systems: Based on the Anumana method of examination, Expert systems, knowledge-based systems, and developing decisions based on these systems are currently used in Rog Nidaan. [9] Expert systems have the following aspects where the information processing is done - The questionnaire has three parts: ...
Introduction: Going by the trend, the Indian traditional medicine system Ayurveda is observing a paradigm shift in its growth, and in the era of technology AI (Artificial Intelligence) is one of the main factors behind it. Be it the discovery of new medicines, implementation of a new drug discovery model, getting its global acceptance through patents, or the delivery of the final product through AI equipped supply chain model system, the intervention of technology cannot be ruled out. Methodology: The literature survey was done through Google Scholar database, PubMed, and Web of Science. The dig out information about the use of IT in Medical fields and, use of technology in Ayurveda were screened for relevant studies synthesis. Result: At last, we have sorted out the areas of Rog-Nidan that need to be taken care of from a research point of view with the help of information technology. Discussion: In this research paper, an effort has been made to review the areas where technology can play its role, especially in Rog-Nidan. Additionally, the approaches that are being used in Ayurveda where it is being presented as wellness therapy and preventive care have been studied along with identifying the gaps that can be addressed with efficient use of IT-based technologies.
The current healthcare system often fails to account for individual health needs, leading to ineffective preventive measures and dietary guidance. Ayurvedic principles, which focus on the Dosha, offer a profound understanding of an individual's constitution, influencing their health, vulnerability to specific diseases, and ideal dietary choices. This paper explores the evolving intersection of ancient Ayurvedic wisdom and modern technology in the realm of disease diagnosis. Ayurveda, with its emphasis on personalized well-being, has long been a source of holistic health practices. In this context, the study delves into the intricate system of Ayurvedic Dosha analysis and its potential applications in contemporary healthcare. The research introduces an innovative way that seamlessly integrates traditional Ayurvedic pulse examination with state-of-the-art technology. By employing pulse sensors and advanced algorithms, the system not only identifies specific ailments but also classifies patients into Ayurvedic Prakriti types. Going beyond conventional diagnosis, this holistic approach extends to personalized recommendations, encompassing diet, lifestyle, Ayurvedic treatments, exercise, and daily routines. While addressing the challenges of harmonizing ancient principles with modern technology, the paper also presents the performance metrics of the model. The accuracy rates are as follows: Logistic Regression (LR) - 85.94%, Random Forest - 89.21%, Decision Tree - 99.70%, and k-Nearest Neighbors (KNN) - 86.43%. These metrics underscore the robustness of the system. In addition to outlining core concepts, methodologies, and model accuracies, the study explores current trends and recent developments in the field, offering readers a comprehensive understanding of Ayurvedic Dosha-based disease diagnosis. The research contributes to the broader discourse on healthcare by paving the way for early detection and individualized, holistic well-being for patients.
Introduction: Every science or pathy must customised with recent advancements and technical developments to accommodate with changing era. Artificial Intelligence (AI) is the recent advancement and its use can help the ancient wisdom to be more useful. Ayurveda is the ancient pathy to treat diseases and remain healthy. With the help of AI we can use this treasure of knowledge for the sake of mankind. In current study we discuss the scope of AI in Ayurveda system. Aim and objective: To identify major fields of Ayurveda, where AI amalgamation can be beneficial/useful to developed Ayurveda as main stream of health sector in India and global. Material and methods: Source of information was taken from web browser, Ayurveda texts, modern science books, research magazines and few papers related in this context. Conclusion: It can be concluded from this review study that use of AI and recent technological advancement are essential for the fulfilment of lacuna for scientific validation and globalization of Ayurveda. So there is very huge scope of AI as research and development of Ayurveda.
Human Body constitution (prakriti) defines what is in harmony with human nature and what will cause to move out of balance and experience illness. Tridosha defines the three basic energies or principles that determine the function of our body on the physical and emotional levels. The three energies are known as VATT, PITT and KAPH. Each individual has a unique balance of all three of these energies. Some people will be predominant in one, while others will be a mixture of two or more. Ayurveda-dosha studies have been used for a long time, but the quantitative reliability measurement of these diagnostic methods still lags behind. A careful and appropriate analysis leads to an effective treatment. To collect a meaningful data set, a questionnaire with 28 different characteristics is validated by Ayurveda experts. Authors calculate Cronbach alpha of VATT-Dosha, PITT-Dosha and KAPH-Dosha as 0.94, 0.98 and 0.98, respectively to check the reliability of the questionnaire. Authors analyzed questionnaires of 807 healthy persons aged 20-60 years and found 62.1% men and 37.9% women. The class imbalance problem is resolved with oversampling and the equally distributed data set of randomly selected 405 persons is used for the actual experiment. Using computer algorithms, we randomly divide the data set (8:2) into a training set of 324 persons and a test data set of 81 persons. Model is trained using traditional machine learning techniques for classification analysis as Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB) and Decision Tree (DT). System is also implemented using ensemble of several machine learning methods for constitution recognition. Evaluation measures of classification such as root mean square error (RMSE), precision, recall, F-score, and accuracy is calculated and analyzed. On analyzing the results authors find that the data is best trained and tested with CatBoost, which is tuned with hyper parameters and achieves 0.96 precision, 0.95 recall, 0.95 F-score and 0.95 accuracy rate. The experimental result shows that the proposed model based on ensemble learning methods clearly surpasses conventional methods. The results conclude that advances in boosting algorithms could give machine learning a leading future.
1) Background: Diabetes is a common chronic disease and a leading cause of death. Early diagnosis gives patients with diabetes the opportunity to improve their dietary habits and lifestyle and manage the disease successfully. Several studies have explored the use of machine learning (ML) techniques to predict and diagnose this disease. In this study, we conducted experiments to predict diabetes in Pima Indian females with particular ML classifiers. (2) Method: A Pima Indian diabetes dataset (PIDD) with 768 female patients was considered for this study. Different data mining operations were performed to a conduct comparative analysis of four different ML classifiers: Naïve Bayes (NB), J48, Logistic Regression (LR), and Random Forest (RF). These models were analyzed by different cross-validation (K = 5, 10, 15, and 20) values, and the performance measurements of accuracy, precision, F-score, recall, and AUC were calculated for each model. (3) Results: LR was found to have the highest accuracy (0.77) for all 'k' values. When k = 5, the accuracy of J48, NB, and RF was found to be 0.71, 0.76, and 0.75. For k = 10, the accuracy of J48, NB, and RF was found to be 0.73, 0.76, 0.74, while for k = 15, 20, the accuracy of NB was found to be 0.76. The accuracy of J48 and RF was found to be 0.76 when k = 15, and 0.75 when k = 20. Other parameters, such as precision, f-score, recall, and AUC, were also considered in evaluations to rank the algorithms. (4) Conclusion: The present study on PIDD sought to identify an optimized ML model, using with cross-validation methods. The AUC of LR was 0.83, RF 0.82, and NB 0.81). These three were ranked as the best models for predicting whether a patient is diabetic or not.
This paper presents the analysis of real-life medical big data obtained from a hospital in central China from 2013 to 2015 for risk assessment of cerebral infarction disease. We propose a new recurrent convolutional neural network(RCNN)-based disease risk assessment multimodel by utilizing structured and unstructured text data from the hospital. In the proposed model, the convolutional layer becomes a bidirectional recurrent neural network(RNN) by utilizing intra-layer recurrent connection within the convolutional layer. Each neuron within convolutional layer receives feed forward and recurrent inputs from the previous unit and neighborhood, respectively. In addition to step-by-step recurrent operation, the region of context capture increases, thereby facilitating fine-grain feature extraction. Furthermore, we use data parallelism approach over multimodel data during training and testing of proposed model. Results shows that data parallelism approach lead to fast conversion speed. The RCNN-based model works differently from traditional convolutional neural network(CNN) and other typical methods. The proposed model exhibits prediction accuracy of 96.02%, which is higher than those of typical existing methods.
Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.
Lifestyle diseases are ailments that are primarily based on the day to day habits of people. Habits that detract people from activity and push them towards a sedentary routine can cause a number of health issues that can lead to chronic non-communicable diseases that can have near life-threatening consequences. The world population has gained more than a decade of life expectancy since 1980, rising to 69.0 years in men and 74.8 years in women in 2015. The rate of people dying from cardiovascular disease and cancers has also fallen, although at a slower pace. An estimated 17.5 million people died from cardiovascular diseases in 2012, representing 31 percent of all global deaths. This number is expected to increase to more than 23 million by 2030. The number of annual deaths has increased from roughly 48 million in 1990 to almost 56 million in 2015. 70% (40 million) of global deaths in 2015 were due to non-communicable diseases. People are spending more years living with illness and disability. High blood pressure, smoking, high blood sugar, high body mass index, and childhood undernutrition were the world's leading risk factors for premature death and ill health in 2015. Innovative approaches and effective and evidence based interventions are required for the prevention, control and treatment of cardiovascular diseases to reduce the burden and save lives. There is need to develop broad and sustainable strategy for cardiovascular research and prevention. Such a strategy should recognize the unique societal influences, regional cultural diversity and the changing lifestyles as rates of urbanization continue to increase dramatically. The involvement of the medical community in prevention efforts is important. Strong methods to control the use of and exposure to tobacco, coupled with promotion of healthy lifestyles, such as increased physical activity and decreased fat and carbohydrate consumption, should be an integral part of any national program. A comprehensive strategy should be designed so that all stages of the life cycle are targeted. Reducing demand for tobacco products and content of salt in foods can help millions of people avoid unnecessary death and suffering from cardiovascular disease.
In many clinical decision support systems, a two-layer knowledge base model (disease-symptom) of rule reasoning is used. This model often does not express knowledge very well since it simply infers disease from the presence of certain symptoms. In this study, we propose a three-layer knowledge base model (disease-symptom-property) to utilize more useful information in inference. The system iteratively calculates the probability of patients who may suffer from diseases based on a multisymptom naive Bayes algorithm, in which the specificity of these disease symptoms is weighted by the estimation of the degree of contribution to diagnose the disease. It significantly reduces the dependencies between attributes to apply the naive Bayes algorithm more properly. Then, the online learning process for parameter optimization of the inference engine was completed. At last, our decision support system utilizing the three-layer model was formally evaluated by two experienced doctors. By comparisons between prediction results and clinical results, our system can provide effective clinical recommendations to doctors. Moreover, we found that the three-layer model can improve the accuracy of predictions compared with the two-layer model. In light of some of the limitations of this study, we also identify and discuss several areas that need continued improvement.
Lifestyle diseases are ailments that are primarily based on the
day to day habits of people. Habits that detract people from activity
and push them towards a sedentary routine can cause a number of
health issues that can lead to chronic non-communicable diseases
that can have near life-threatening consequences.
Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behaviour with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labour. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology—up to and including today's “omics”. AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.
Ayurveda is considered as one of the oldest of the traditional systems of medicine (TSMs) accepted worldwide. The ancient wisdom in this traditional system of medicine is still not exhaustively explored. The junction of the rich knowledge from different traditional systems of medicine can lead to new avenues in herbal drug discovery process. The lack of the understanding of the differences and similarities between the theoretical doctrines of these systems is the major hurdle towards their convergence apart from the other impediments in the discovery of plant based medicines. This review aims to bring into limelight the age old history and the basic principles of Ayurveda. This would help the budding scholars, researchers and practitioners gain deeper perspicuity of traditional systems of medicine, facilitate strengthening of the commonalities and overcome the challenges towards their global acceptance and harmonization of such medicinal systems. © 2016 Center for Food and Biomolecules, National Taiwan University.
Ayurveda, a traditional system of medicine that originated over three millennia ago in the South Asian region, offers extensive insights about food and health based on certain unique conceptual as well as theoretical positions. Health is defined as a state of equilibrium with one’s self (svasthya) but which is inextricably linked to the environment. Ayurvedic principles, such as the tridosa (three humors) theory, provide the relationship between the microcosm and the macrocosm that can be applied in day-to-day practice. Classical Ayurveda texts cover an array of themes on food ranging from diversity of natural sources, their properties in relation to seasons and places and to their specific function both in physiological and pathological states. The epistemic perspective on health and nutrition in Ayurveda is very different from that of biomedicine and modern nutrition. However, contemporary knowledge is reinventing and advancing several of these concepts in an era of systems biology, personalized medicine, and the broader context of a more holistic transition in sciences in general. Trans-disciplinary research could be important not only for pushing the boundaries of food and health sciences but also for providing practical solutions for contemporary health conditions. This article briefly reviews the parallels in Ayurveda and biomedicine and draws attention to the need for a deeper engagement with traditional knowledge systems, such as Ayurveda. It points out that recreation of the methodologies that enabled the holistic view point about health in Ayurveda may unravel some of the complex connections with Nature.
In wake of the fact that despite the availability of advanced technologically aided modern medicine system, our society is witnessing a rise in various lifestyle disorders, chronic diseases and development of resistance to the existing remedies; integration of ayurveda with allopathy seems the need of the hour. A novel comprehensive system, if generated by fusion of rich traditional therapies with modern medicine system, can utilize the best of both the systems without disrespecting any and can provide care in disease management, disease prevention, preservation, protection and health rejuvenation. Amalgamation of ayurveda with allopathy will certainly be able to create a healthy society by developing a healthcare system that is more effective than either system used alone as well as less expensive and less toxic.
This study assesses the interrater reliability of Ayurvedic pulse (nadi), tongue (jivha), and body constitution (prakriti) assessments. Fifteen registered Ayurvedic doctors with 3-15 years of experience independently examined twenty healthy subjects. Subjects completed self-assessment questionnaires and software analyses for prakriti assessment. Weighted kappa statistics for all 105 pairs of doctors were computed for the pulse, tongue, and prakriti data sets. According to the Landis-Koch scale, the pairwise kappas ranged from poor to slight, slight to fair, and fair to moderate for pulse, tongue, and prakriti assessments, respectively. The average pairwise kappa for pulse, tongue, and prakriti was 0.07, 0.17, and 0.28, respectively. For each data set and pair of doctors, the null hypothesis of random rating was rejected for just twelve pairs of doctors for prakriti, one pair of doctors for pulse examination, and no pairs of doctors for tongue assessment. Thus, the results demonstrate a low level of reliability for all types of assessment made by doctors. There was significant evidence against random rating by software and questionnaire use and by the diagnosis preferred by the majority of doctors. Prakriti assessment appears reliable when questionnaire and software assessment are used, while other diagnostic methods have room for improvement.
A physician's job is to know the human body and its functions in terms of Prakritisthata (homeostasis). The four cornerstones of diagnostic medicine, each essential for understanding homeostasis, are: anatomy (the structure of the human body), physiology (functions of Dosha, Dhatu and Mala), pathology (what can go wrong with the anatomy and physiology) and psychology (thought and behavior). Once the doctor knows what is normal and can measure the patient's current condition against those norms; she or he can then determine the patient's particular deviation from homeostasis and the degree of deviation. This is called diagnosis. Once a diagnosis has been reached, the doctor is able to propose a management plan, which will include treatment as well as plans for follow-up. From this point on, in addition to treating the patient's condition, the doctor educates the patient about the causes, progression, outcomes, and possible treatments of his ailments, as well as providing advice for maintaining health.
This is an analytical study of the Diagnostic methods Prescribes in Ayurveda. As in the case of disease and treatments the concept of diagnosis also is unique in Ayurveda. It goes to the Nidana of Doshicimbalance by studying the physical, physiological, psychic and behavoural aspects of the patient. The paper gives an insight into the various diagnostic methods enunciated in Sastras which turns out to be a fore-runner of any of modern diagnostic methods.
Importance
Surgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making.
Observations
Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process.
Conclusions and Relevance
Integration of artificial intelligence with surgical decision-making has the potential to transform care by augmenting the decision to operate, informed consent process, identification and mitigation of modifiable risk factors, decisions regarding postoperative management, and shared decisions regarding resource use.
The present practice of Ayurveda emphasizes on traditional way. The globalisation, patent, intellectual property rights issues and biopiracy are becoming major challenges in the indigenous traditional medical system like Ayurveda. So there is going to be crises and challenges in the Ayurveda system. In order to promote as a global medicine and equip Ayurveda to meet the global healthcare needs of the 21 st century, there is an urgent need to modernise the ancient system in pace with the development of science and technology. So considering all these facts, Ayurveda is needed to restructure in the global context to meet the rising demands of a cyber society with the application of information and communication technology.
Complementary and alternative medicine (CAM) therapies are classified into five major domains: alternative medical systems (which includes traditional Chinese medicine, ayurveda, homeopathy, and naturopathy); mind-body interventions (including meditation, hypnosis, dance music, and art therapy); biologically based treatments (such as vitamins, minerals, herbs, and vegetarianism); manipulative and body-based methods (including chiropractic and massage therapies); and energy therapies - with some overlap across categories. Biomedical engineering (BME) is essential for exploring the safety and efficacy of these CAM therapies in the context of rigorous science. Recently, integrative medicine (IM) has been considered the ideal medical system for integrating conventional medicine and CAM. For this purpose, the concept of how to integrate these methods first must be investigated, and independent verification of CAM methods will be necessary. Biomedical engineering will be expected to be the most important method for verifying CAM scientifically.
Alternative medicine: The definitive guide
- Burton Goldberg
A Handbook of Ayurveda
- Bhagvan Dash
- M M Junius
Deep feature learning for disease risk assessment based on convolutional neural network with intra-layer recurrent connection by using hospital big data
- Mohd Usama
- Jiafu Ahmad
- M Wan
- Shamim
- Mohammed F Hossain
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