attribute
∗p-value < 0.05 is statistically significant.
Python 3.7 programming language was used for building ML-based heart disease prediction system. Powerful software libraries supported by Python namely NumPy, Pandas, Seaborn, Statsmodels.api, SciPy and Sklearn etc. were used for exploratory analysis of data 17 and implementing five ML algorithms namely k-Nearest Neighbours (k-NN), Naïve Bayes (NB), Logistic Regression (LR), AdaBoost (AB) and Random Forest (RF). This study also has typical binary classification where 13 input attributes are observed to determine if there is a high risk of heart disease in a patient (risk of CVD = high) or not (risk of CVD = low). Fig. 1 shows the workflow diagram of complete project.
Workflow diagram of the study. This figure depicts the complete workflow of the study. The medical data set of 1670 records were gathered (in random fashion). Seventy percent data samples used to train the models. Test subset comprised the rest 30% of medical records. Five machine learning algorithms are applied to train the training subset. The prediction system was hosted on the public cloud for easy accessibility.
It was observed that there were no missing values or outliers in the data.
Since the ML algorithms can process only numerical data, the categorical attributes were label encoded. Gender female was encoded as 1 while male as 0. For all the other categorical variables like diabetes, stress, exercise etc., the presence (yes) was encoded as 1 while absence (no) was encoded as 0. High risk of CVD was encoded as 1 while low risk of CVD was encoded as 0.
Using the train_test_split function supported by scikit learn library, the complete medical data set was randomly split into two portions in the ratio 70:30 referred as training and test/validation subset, respectively. Out of total 1670 records, training subset had 1169 records while test subset had 501 records. Detailed information about the training and test subsets is provided in Table 3 . The total number of records in the training data set were 1169, of which 656 records correspond to CVDs while 513 records belonged to healthy people not diagnosed with CVDs.
Details of training and test subsets.
Class | Training subset (70%) | Test subset (30%) | Total records |
---|---|---|---|
High-risk cardiovascular disease (CVD) | 656 | 237 | 893 |
Low-risk CVD | 513 | 264 | 777 |
Total records | 1169 | 501 | 1670 |
ML algorithms with well demonstrated performance for classification namely NB, LR and k-NN were applied to build the prediction model.
Research has proved that the performance of a ML-based prediction system can be improvised using ensembling techniques. 18 Ensembling is a union of individual classifying algorithms. Bagging ensemble algorithms namely RF and boosting ensemble algorithms namely adaptive boosting AB were also implemented for enhanced performance.
The performance of prediction models developed using k-NN, NB and LR algorithms was analysed using the validation subset of 501 records as shown in Table 4 . Of these records, 237 were confirmed cases of CVDs while remaining 264 records correspond to healthy people not diagnosed with CVDs. Prevalence of disease in validation subset was 237/501 = 47.3%
Performance of machine learning algorithms on validation set of 501 records.
Algorithm | True positive | True negative | False negative | False positive | Sensitivity | Specificity | Positive predictive value | Negative predictive value | Accuracy |
---|---|---|---|---|---|---|---|---|---|
k-Nearest Neighbours | 211 | 230 | 26 | 34 | 89% | 87.1% | 86.1% | 89.8% | 88% |
Naïve Bayes | 210 | 232 | 27 | 32 | 88.6% | 87.8% | 86.7% | 89.5% | 88.2% |
Logistic Regression | 215 | 240 | 22 | 24 | 90.7% | 90.9% | 89.9% | 91.6% | 90.8% |
AdaBoost | 218 | 246 | 19 | 18 | 91.9% | 93.1% | 92.3% | 92.8% | 92.6% |
Random Forest | 220 | 250 | 17 | 14 | 92.8% | 94.6% | 94% | 93.6% | 93.8% |
Analysis of confusion matrix is a standard way to check the performance of ML-based prediction system. Confusion matrix has four components namely true positives (TPs), true negatives (TNs), false positives (FPs) and false negatives (FNs).
TPs: Heart patients who are predicted correctly to have heart diseases.
TNs: Healthy persons who are predicted correctly to be healthy.
FPs: Healthy persons predicted incorrectly to have heart diseases (Type 1 error).
FNs: Heart patient predicted incorrectly to be healthy (Type 2 error).
These values are used to calculate accuracy, specificity, sensitivity, positive predictive value (PPV) and negative predictive value (NPV). PPV and NPV depend on the prevalence of disease.
A brief description of these parameters is given below.
Grid Search for cross-validation was used to identify the best hyperparameters for the learning algorithms. Grid Search CV class from sklearn library was used for this purpose.
The best performance prediction system built using RF model was deployed in Microsoft Azure cloud for better accessibility. 19 ‘ Pickle ’ and ‘ Flask ’ software libraries of Python programming language were used for this purpose. 20 Hosting the prediction system on cloud enables it to be easily accessed from anywhere in the world via Internet. This is highly useful feature for healthcare sector of India, which faces the major issue of shortage of medical facilities especially in rural areas. Accessing this prediction system is as easy as accessing an e-mail via Internet.
CVD prediction system was developed by applying five well-established ML algorithms on the training data set. The performance was tested on the validation test set of 501 records. Prevalence of disease in validation subset was 237/501 = 47.3% Performance metrics namely accuracy, sensitivity, specificity, PPV and NPV were calculated for each algorithm. The performance results of all classifiers are given in Table 4 .
The best hyperparameters for k-NN (n_neighbors = 12) resulted in a performance of sensitivity 89%, specificity 87.1%, PPV 86.1%, NPV 89.8%. The performance of NB was found to better than k-NN. Sensitivity 88.6%, specificity 87.8%, PPV 86.7%, NPV 89.5% were achieved by NB.
LR with hyperparameters (C = 1, penalty = l2) performed well in classifying people with low risk or high risk of CVDs. LR correctly classified 455 out of 501 records, thus attaining a classification accuracy of 90.8%. Sensitivity 90.7% and specificity were 90.7% and 90.9%, respectively. PPV was observed to be 89.9% while NPV was 91.6%.
Models built using ensemble techniques (RF and AB) performed better than LR. AB model was trained with Stage-wise Adaptive Modelling using a Multi-class Exponential loss function (n_estimators = 30) while RF based on ‘gini index’ with n_estimators = 150 resulted in the best performance. Sensitivity and specificity of AB model was 91.9% and 93.1%, respectively, while RF reported 92.8% sensitivity and 94.6% specificity. PPV 94% and NPV 93.6% were achieved by RF–based prediction model.
Interpretation of ML-based models is not easy, and these are usually considered as ‘black boxes. However, logistic regression–based models are quite interpretable. Logistic regression was implemented using the Logit function (Binomial family) based on maximum likelihood estimation method to predict CVD risk using statsmodels.api library of Python. Fig. 2 shows the summary of results obtained.
Study population characteristics mean (standard deviation) of numerical attributes along with p-values of t -test to indicate the statistical significance for two groups: high risk/low risk of cardiovascular disease (CVDs). Count (%) of categorical attributes in two groups: high risk/low risk of CVDs.
Male gender, diabetes, hypertension, high cholesterol level, smoking and alcohol were significantly associated with CVD. Lack of exercise and stress were observed to be more prevalent in CVD group (p value < 0.05).
Estimate column in the summary reflects the natural logarithm of odds ratio of getting diagnosed with high risk of heart disease keeping all other features constant. Due to negative values of log (odds ratio) it is inferred that females had a low risk of CVDs compared with males. Regular exercise and intake of healthy diet were observed to be associated with low risk of CVDs; on the other hand, diabetes, hypertension, stress, smoking and family history tend to result in high risk of CVDs.
The odds ratio column in the summary suggests how the odds ratio of being detected with high risk of CVD change if all other attributes are kept constant. Hypertension tends to increase the odds ratio of high risk of CVDs by 1.573 while the odds ratio drops significantly to 0.328 with regular physical exercise. Odds ratio of high risk of CVD for females is 0.788 compared with males.
Ensemble algorithms (RF and AB) are based on decision trees and attribute importance is graded according to selection occurrence frequency of an attribute as a decision node decided based on information gain and entropy. Variable importance for boosting algorithm was decided based on the impurity-based scores using feature_importances_ from sklearn library of Python. Attributes exercise, weight, total cholesterol, hypertension and age were the top five important attributes for AB algorithm. In case of RF prediction system, variable importance scores for attributes weight, exercise, total cholesterol, hypertension, and gender were found to be maximum for predicting CVDs. Variable importance for AB algorithms and RF is represented graphically in Fig. 3 (a) and (b), respectively.
Variable importance. (a) Variable importance for AdaBoost-based prediction model. (b) Variable importance for Random Forest–based prediction model.
RF-based CVD prediction model (trained on 1169 records and tested on 501 records) is hosted on cloud and can be easily accessed at das.southeastasia.cloudapp.azure.com/predict/
The input attributes of the patient are entered into the system. The system predicts if the patient has low risk of CVDs or high risk. Sample screenshots of the result obtained using the prediction system are shown in Fig. 4 .
Using cardiovascular disease (CVD) prediction model to test the risk of CVDs. The medical practitioner enters the patient's clinical parameters as well as attributes related to his lifestyle to predict the risk of CVD.
In the recent years, substantial research studies have been carried out to build methods for diagnosing heart diseases in early stages. Various feature selection techniques were applied in the research carried out by Takci. 21 (2018), and the resulting prediction system attained an accuracy of 84.81%. Similar study was carried out by Kausar et al. and an accuracy of 88.41% was obtained. 22 Prediction system developed by Khalid Raza using ensembling technique (2019) attained an accuracy of 88.88%. 23 A similar accuracy level of 89% was achieved by the prediction system developed by Haq et al. in 2019. 24 Using artificial neural network to design a prediction system Alic et al. achieved an accuracy of 91% in their research study. 25 But importantly, the prediction system developed in all of these studies do not work effectively well for Indian population as these models are based on data collected from Western countries and do not take into consideration lifestyle-related risk factors responsible for CVDs (lack of physical activity, family history, alcohol etc.). Moreover, these systems rely on the results of medical tests like ECG, treadmill test, fluoroscopy tests etc., which are not feasible in Indian primary health centres in the existing scenario.
The accuracy attained in the present study is 93.8%. The prediction system developed in this research uses 13 clinical parameters and identifies the risk of a person to have heart disease. Compared with the studies done so far, this study has been carried out on Indian population, and the potential risk factors like high body weight, lack of exercise, psychological stress, family history, smoking and alcohol consumption habits have been considered in this study (unlike the studies quoted previously). It is worth noting that the system developed in this study is highly cost-effective compared with earlier studies as expensive tests like fluoroscopy and treadmill tests have not been taken into consideration. Easy accessibility of the prediction system via Internet is also an added remarkable feature of this study, which was not reported by earlier studies. It is worth mentioning here that prediction model developed in this pilot study predicts output depending on the study population attribute trends it was trained on. Once the ML models are trained and tested on voluminous data sets, it can be used as a screening tool in rural India and can help in the prevention of CVDs.
Cost-effectiveness, excellent performance and easy accessibility of the prediction system via Internet defend the use of ML-based prediction system as a screening tool for CVD detection in India.
To the best of our knowledge, this study was first of its kind in Indian context. Developed countries like the United Kingdom and the United States are investing their resources to carry to research for developing ML-based prediction models for diagnosing heart diseases in primary healthcare centres. 26 , 27 It is recommended that similar studies should be promoted in India. The current national health policy (2017) of our government, laying stress on preventive health will be more meaningful and fruitful if advancement in this field is made as early as possible. 28 We propose larger studies of multicentric nature for development of AI prediction systems for CVD screening in our country, which is facing ever increasing load of morbidity and mortality due to CVD being detected in late advanced stages. Premier institutes of medicine and technology can collaborate in this regard to diagnose other lifestyle diseases and non-communicable diseases like malignancies. Cardiological Society of India (CSI) can help in this regard. Other modern techniques like artificial neural networks can be applied to further improve the performance of the system.
This study used a data set of 1670 patients reporting to a tertiary care private setup in a south Indian metropolitan city where largely the higher income group seeks the medical care. This potentially may seem biased in reader's mind, but this study was aimed only to detect the robustness of a prediction model based on ML. The results obtained from the prediction system developed in this study are based on the attribute trends of the study population on which the model is trained on. In future the model needs to be trained on huge data sets collected from diverse regions before using it as a screening tool.
The study portrays the capability of ML algorithms to predict CVDs in Indian population. Issues of affordability and accessibility in healthcare sector of India can be addressed using ML-based models, which can be easily accessed via Internet even in the rural parts of the country. It is proposed to build and test the performance of similar systems using voluminous cardiac data sets belonging to all economic sections of the society collected from various regions of India. We recommend similar studies of multicentric nature across entire country. To achieve the sustainable development goals laid down by World Health Organization, it is high time, we as a country do take timely advantage of ML-based prediction systems in improving preventive care aspect of public healthcare system. 29
ML-based tools have shown remarkable performance in diagnosing various serious diseases in initial stages in healthcare centres of developed countries.
An indigenous high-performance ML-based CVD prediction system easily accessible via Internet is proposed for existing Indian healthcare system. Healthcare in India can be made more affordable and accessible using ML-based prediction systems.
The authors have none to declare.
The authors express their heartfelt gratitude to Sagar Hospitals, Jayanagar, Bengaluru, for providing anonymized information of patients' health parameters for carrying out this study. No funding was received for this project.
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Contrary to current insulin formulations, endogenous insulin has direct access to the portal vein, regulating glucose metabolism in the liver with minimal hypoglycaemia. Here we report the synthesis of an amphiphilic diblock copolymer comprising a glucose-responsive positively charged segment and polycarboxybetaine. The mixing of this polymer with insulin facilitates the formation of worm-like micelles, achieving highly efficient absorption by the gastrointestinal tract and the creation of a glucose-responsive reservoir in the liver. Under hyperglycaemic conditions, the polymer triggers a rapid release of insulin, establishing a portal-to-peripheral insulin gradient—similarly to endogenous insulin—for the safe regulation of blood glucose. This insulin formulation exhibits a dose-dependent blood-glucose-regulating effect in a streptozotocin-induced mouse model of type 1 diabetes and controls the blood glucose at normoglycaemia for one day in non-obese diabetic mice. In addition, the formulation demonstrates a blood-glucose-lowering effect for one day in a pig model of type 1 diabetes without observable hypoglycaemia, showing promise for the safe and effective management of type 1 diabetes.
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This work was supported by grants from the National Key R&D Program of China (2022YFE0202200, J.W.), JDRF (2-SRA-2021-1064-M-B, Z.G.; 2-SRA-2022-1159-M-B, J.W.), the Key Project of Science and Technology Commission of Zhejiang Province (2024C03083, Z.G.; 2024C03085, J.W.), Zhejiang University’s start-up packages and the Starry Night Science Fund at Shanghai institute for Advanced Study of Zhejiang University (SN-ZJU-SIAS-009, J.W.). A.R.K. is supported by the National Center for Advancing Translational Sciences, National Institutes of Health (KL2TR002490, J.W.). The project was supported by the Clinical and Translational Science Award program of the National Center for Advancing Translational Science, National Institutes of Health (UL1TR002489, J.W.). We appreciate the help from J. Pan and D. Wu of the Research and Service Center (College of Pharmaceutical Science, Zhejiang University) for technical support, G. Z. and Y. Zhang (Cryo-EM centre, Zhejiang University) for processing the samples for electron microscopy and D. Xu, M. Zhang, S. Xiong and D. Chen (Disease Simulation and Animal Model Platform of Liangzhu Laboratory) for taking care of the minipigs.
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State Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
Kangfan Ji, Xiangqian Wei, Juan Zhang, Yang Zhang, Jianchang Xu, Xinwei Wei, Wei Liu, Yanfang Wang, Yuejun Yao, Xuehui Huang, Shaoqian Mei, Yun Liu, Shiqi Wang, Zhengjie Zhao, Ziyi Lu, Jiahuan You, Guangzheng Xu, Jinqiang Wang & Zhen Gu
Jinhua Institute of Zhejiang University, Jinhua, China
Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Anna R. Kahkoska
Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China
Youqing Shen
Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, USA
John. B. Buse
Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
Jinqiang Wang & Zhen Gu
Department of Pharmacy, Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
Jinqiang Wang
Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
Liangzhu Laboratory, Hangzhou, China
Institute of Fundamental and Transdisciplinary Research, Zhejiang University, Hangzhou, China
MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, China
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Z.G., J.W., Y.S. and J.B.B. conceived and designed the study. K.J., Xiangqian Wei, J.Z., J.X., Xinwei Wei, Y.Z., W.L., Y.W., Y.Y., S.M. and Y.L. conducted experiments and obtained related data. X.H., S.W., Z.Z., J.Y., G.X. and Z.L. gave experimental operation and theoretical guidance of mice experiments. K.J., Xiangqian Wei, J.Z. and J.X. conducted minipigs experiments and provided theoretical support. Z.G., J.W., Y.S., K.J., J.Z., Xiangqian Wei, A.R.K., J.B.B. and J.X. analysed the data and wrote the paper.
Correspondence to Jinqiang Wang or Zhen Gu .
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Z.G. is the co-founder of Zenomics Inc., Zcapsule Inc. and μ Zen Inc. The other authors declare no competing interests.
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Extended data fig. 1 bg-regulating effects in diabetic minipigs..
BG of diabetic minipigs treated with the insulin capsules (oral), the PPF-ins capsules (oral) or Lantus (s.c.). The insulin dose of oral formulations was set to 4.2 U/kg. The Lantus dose was set to 0.3 U/kg.
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Ji, K., Wei, X., Kahkoska, A.R. et al. An orally administered glucose-responsive polymeric complex for high-efficiency and safe delivery of insulin in mice and pigs. Nat. Nanotechnol. (2024). https://doi.org/10.1038/s41565-024-01764-5
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Explore the latest advances in PCI and device therapy, treatment of myocardial infarction, dyslipidemia, heart failure, afib & rhythm disorders, TAVR, and ... 1990-2017 Global Disparities in Parkinson Disease Guidelines for ... and Policy Promoting EDI in Genetics Research PTSD and Cardiovascular Disease Red Blood Cell Transfusion: 2023 AABB ...
Introduction and background. Atherosclerosis is the process of formation of plaque composed of cholesterol, fat, calcium, and other substances in the wall of large and medium-sized arteries causing diminished blood flow to an area of the body [1,2].Moreover, atherosclerotic vascular disease can be classified into two arenas: a cardiovascular disease affecting the heart and peripheral blood ...
Heart disease is a fatal human disease, rapidly increases globally in both developed and undeveloped countries and consequently, causes death. ... research study recommends that which feature ...
Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular ...
BACKGROUND: The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic ...
In clinical research, patient management after myocardial infarction (MI) and outside the hospital are leading topics, but the true 'hot' topic was aortic valve disease that saw a surge of interest, related to transaortic valve repair, starting 2008. Though still small in numbers, heart failure research and stem cells saw substantial growth.
In this Review, Redfield and colleagues highlight the importance of studying human cardiac tissue in HFpEF, discuss sources, challenges and methods for studying human myocardial samples, summarize ...
Aficamten is a small-molecule cardiac myosin inhibitor designed to treat hypertrophic cardiomyopathy. Hartman et al. use mouse models of cardiac function to show that aficamten decreases the ...
Introduction. Cardiovascular diseases (CVDs) have collectively remained the leading causes of death worldwide and substantially contribute to loss of health and excess health system costs. 1, 2 The Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study has tracked trends in death and disability since 1990 and has provided an updated perspective on the status of cardiovascular health ...
Abstract. Cardiovascular diseases (CVDs) are the leading cause of premature death and disability in humans and their incidence is on the rise globally. Given their substantial contribution towards the escalating costs of health care, CVDs also generate a high socio-economic burden in the general population.
Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to ...
Symptoms are subjective experiences that may indicate disease or significant change in health status. Symptoms have been linked to cardiovascular disease (CVD) since Egyptian physicians and Hippocrates described fatigue and dyspnea, respectively, as being related to the failing heart. 1,2 In a contemporary view of CVD, symptoms often are critical elements of the diagnosis, evaluation ...
Cardiovascular diseases articles from across Nature Portfolio. Atom; RSS Feed; Definition. Cardiovascular diseases are pathological conditions affecting the heart and/or blood vessels that is, the ...
Heart disease, including coronary heart disease, remains the leading cause of death in the United States. However, the rate of heart disease deaths has declined by 70% over the past 50 years, thanks in part to NHLBI-funded research. Many current studies funded by the NHLBI focus on discovering genetic associations and finding new ways to ...
Ischaemic heart disease and cerebrovascular disease (stroke) combined accounted for more than 85.1% of all cardiovascular disease deaths in 2016. Our study indicated that CVDs(angina, stroke) were prevalent and variable among older adults in six countries. Angina and stroke were both highest in Russian Federation(47.5%, 6.1% respectively).
Cardiovascular disease (CVD) is an umbrella term for a number of linked pathologies, commonly defined as coronary heart disease (CHD), cerebrovascular disease, peripheral arterial disease, rheumatic and congenital heart diseases and venous thromboembolism. Globally CVD accounts for 31% of mortality, the majority of this in the form of CHD and ...
Heart diseases are consistently ranked among the top causes of mortality on a global scale. Early detection and accurate heart disease prediction can help effectively manage and prevent the disease. However, the traditional methods have failed to improve heart disease classification performance. So, this article proposes a machine learning approach for heart disease prediction (HDP) using a ...
Interdepartmental Hypertension Research Center (CIRIAPA), 'Federico II' University, Naples, Italy. Center for Basic and Clinical Immunology Research (CISI), 'Federico II' University, Naples, Italy. Search for more papers by this author
The science of heart health focuses on several related biological phenomena, including inflammation, cholesterol level and a lingering form of immune response that tends to exacerbate the risk of ...
References. Since its inception, articles published in Arteriosclerosis, Thrombosis, and Vascular Biology (ATVB) have contributed to our understanding of coronary artery disease (CAD) and its different complex pathophysiological processes. Here, we review articles related to CAD published in ATVB in the past 2 years from 2018 to 2019.
In a review paper, the American Heart Association spells out how symptoms of heart attack, stroke and other cardiovascular problems differ in women and men. Aug. 22, 2022, 4:09 PM UTC / Source ...
Introduction. Cardiovascular diseases (CVDs) are the foremost reason of disease burden and mortality all over the world. Approximately 30% of total deaths (17.9 million) occurred due to CVDs globally in 2016. 1 The situation is critically serious in low- and middle-income countries like India. During the past three decades, the number of deaths due to CVDs has increased significantly from 15.2 ...
Coronary artery disease (CAD) is the world-wide leading cause of death not only in high-income countries but also increasingly in developing countries. 1 Although death rates from CAD have decreased in most high- and middle-income countries in the past 2 decades, there are worrying signs of a lessening trend in the United States, 2 and the dramatic increases of world-wide obesity 3 and ...
The major organs included heart, lung, liver, spleen, kidney (from up to down, left) and intestine (right). ... (UL1TR002489, J.W.). We appreciate the help from J. Pan and D. Wu of the Research ...