Volume-11 , Special Issue-01 , Nov - 2023 Go Back

One of the key drivers of the health information revolution in the healthcare industry is cloud computing. Cloud computing makes it possible for anyone to exchange digitized health records globally. This technology fosters innovation and enhances safety in the healthcare industry. The use of this technology makes communication with the health matrix possible on a global scale. For many years, cloud computing has been used in the healthcare industry and has developed along with changes in the business. Through a network connection, this technology creates universally usable hardware for a range of healthcare applications. Doctors can provide their patients with health advice and communicate their daily health routines, often maintaining the health of their bodies and brains. By using videoconferencing, psychologists and psychiatrists may create a pleasant environment for their patients. The necessity for cloud computing in healthcare is covered in this essay. The large key is that the benefits, drawbacks, and difficulties of cloud computing for the healthcare sector are noted. Finally, it discusses the important uses of cloud computing in the field of healthcare. Increasingly, healthcare providers are giving Internet of Things (IoT)-equipped devices to patients, and by connecting these devices to the cloud systems of hospitals, patient data is promptly sent to their physicians. Because of this, cloud computing enhances efficiency and multiplies the methods for streamlining healthcare delivery. These fast-evolving technologies include Big Data analytics, artificial intelligence, and the Internet of Medical Things. It increases interoperability, increases resource availability, and lowers costs.

Cloud computing, Cloud system, Healthcare organizations, Electronic health records, Security and privacy, HIPAA, e-Health, Health Monitoring, Telemedicine

[1] Seymour, Dr. Tom; Frantsvog, Dean; and Graeber, Tod. Electronic Health Records (EHR) 10.19030/ahs. v3i3.7139, 2014.
[2] “Health Insurance Portability and Accountability Act of 1996 (HIPAA),” Centers for Medicare and Medicaid Services, 1996.
[3] Ppapersnt Tyagi, Navdeep Aggarwal, Bhanu P. Dubey, and Emmanuel S. Pilli “HIPAA Compliance and Cloud Computing”, International Journal of Computer Applications, 2013.
[4] Sutton, R.T., Pincock, D., Baumgart, D, C., et al. “An overview of clinical decision support systems: benefits, risks, and strategies for success”. npj Digit. Med. 3, 17, 2020.
[5] Opara-Martins, Justice; Sahandi, R.; and Tian, Feng (2014), Critical Review of Vendor Lock-in and its Impact on Adoption of Cloud Computing, International Conference on Information Society, i-Society 2014, 10.1109/i-Society.2014.7009018
[6] Naren, J., Sowmya, S.K., and Deepika, P. (2014) “Layers of Cloud: IaaS, PaaS, and SaaS: A Survey”. International Journal of Computer Science and Information Technology, Vol.5, Issue.3, pp.4477–4480, 2014.
[7] Rahimli, Ailar (2014), A Review of Cloud Computing Technology Solutions for Healthcare Research, Journal of Applied Sciences, Engineering, and Technology, 8, pp.215-253, 2014.
[8] Javaid, Mohd & Haleem, Abid & Singh, Ravi & Rab, Shanay & Suman, Rajiv & Haleem Khan, Ibrahim (2022). Evolutionary Trends in Progressive Cloud Computing-Based Healthcare: Ideas, Enablers, and Barriers International Journal of Cognitive Computing in Engineering, 3. 10.1016/j.ijcce.2022.06.001.
[9] Darwish, Ashraf & Hassanien, Aboul Ella & Elhoseny, Mohamed & Kumar, Arun & Muhammad, Khan (2019). The impact of the hybrid platform of the Internet of Things and cloud computing on healthcare systems: opportunities, challenges, and open problems Journal of Ambient Intelligence and Humanized Computing, 10, 10.1007/s12652-017-0659-1.
[10] Sen, Jaydip (2013). Security and Privacy Issues in Cloud Computing. 10.4018/978-1-4666-4514-1.ch001
[11] Ahuja, Sanjay; Mani, Sindhu; and Zambrano, Jesus (2012). A Survey of the State of Cloud Computing in Healthcare Network and Communication Technologies, 1. 10.5539/nct.v1n2p12.
[12] Okediran, Oladotun & Isaac, Oladimeji & Adeyemi, Sijuade & Wahab, Wajeed (2020). A Framework for a Cloud-Based Electronic Health Records System for Developing Countries, pp.12-13, 2020.
[13] Mishra, Snigdha & Bhutia, Samten & Akhtar, Nadeem & Dhar, Sourav (2019). Cloud-Based Multilayer Telemedicine Architecture: A Case Study: Proceedings of ICCDN 2018 10.1007/978-981-13-3450-4_60.
[14] Butpheng, C.; Yeh, K.-H.; Xiong, H. Security and Privacy in IoT-Cloud-Based e-Health Systems—A Comprehensive Review Symmetry 2020, 12, 1191 https://doi.org/10.3390/sym12071191
[15] Ghasemifard, Najmeh (2014) A cloud solution for medical image processing. Journal of Engineering Research and Applications, 4.
[16] Mehraeen, Esmaeil & Ghazisaeedi, Marjan & Farzi, Jebraeil & Mirshekari, Saghar (2016). Security Challenges in Healthcare Cloud Computing: A Systematic Review Global Journal of Health Science, 9, 157, 10.5539getss.v9n3p157.
[17] Kuo, Mu-Hsing (2011) Opportunities and Challenges of Cloud Computing to Improve Health Care Services Journal of Medical Internet Research, 13. e67, 10.2196/jmir.1867
[18] Cao, Shihua & Lin, Xin & Hu, Keyong & Wang, Lidong & Li, Wenjuan & Wang, Mengxin & Le, Yuchao (2021). Cloud Computing-Based Medical Health Monitoring IoT System Design Mobile Information Systems. 2021. 1–12. 10.1155/2021/8278612.
[19] Abukhousa, Eman & Mohamed, Nader & Al-Jaroodi, Jameela. (2012). e-Health Cloud: Opportunities and Challenges. Future Internet, 4. 621-645. 10.3390/fi4030621.
[20] M. U. Kolhatkar and V. A. Gulhane, "A review on hybrid cloud approach for sharing health information and management," 2017 International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, 2017, pp. 1-4, doi: 10.1109/ICISC.2017.8068609.

Atin Bera, Arya Bhattacharyya, Radhakrishna Jan, Sudipta Kumar Dutta, "A Simplified Review of Cloud Computing in the Healthcare System", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.290-295, 2023.

The condition that is most prevalent nowadays is heart disease, that may be successfully treated if caught and treated at an early enough stage. Heart disease diagnosis requires extreme caution since the procedure might be derailed by human mistake. Machine learning techniques were widely popular in many walks of life, but they rose to prominence in the field of heart disease forecasting. Many biological characteristics included in cardiac patient datasets have little bearing on diagnosis. Prediction accuracy for cardiac patients may be improved while computational complexity is reduced by eliminating irrelevant elements from the available data-set. This technique provides a density-based unsupervised method for identifying cardiac anomalies. The filter-based feature selection strategy is used to begin the process of narrowing down the data collection to its most fundamental characteristics. In order to improve the clustering effectiveness of healthy cases and to detect aberrant examples like cardiac patients, a new method for clustering with adaptive variables called Density Based Clustering has been applied. The DBSCAN method, that generates density-based clusters, is intended to solve these problems; though, the best way to choose an epsilon value and a minimum value is still up for debate. These two factors are used in the suggested strategy to achieve high diagnostic accuracy in patients with cardiac conditions.

Heart Diseases, Diseases Prediction Model, Outlier Data, Machine Learning

[1] A. K. Gárate-Escamila, A. Hajjam El Hassani, and E. Andrès, “Classification models for heart disease prediction using feature selection and PCA,” Informatics in Medicine Unlocked, vol. 19, p. 100330, 2020. doi:10.1016/j.imu.2020.100330
[2] A. N. Repaka, S. D. Ravikanti, and R. G. Franklin, “Design and implementing heart disease prediction using naives bayesian,” 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 2019. doi:10.1109/icoei.2019.8862604
[3] A. Singh and R. Kumar, “Heart disease prediction using machine learning algorithms,” 2020 International Conference on Electrical and Electronics Engineering (ICE3), 2020. doi:10.1109/ice348803.2020.9122958
[4] D. Shah, S. Patel, and S. K. Bharti, “Heart disease prediction using Machine Learning Techniques,” SN Computer Science, vol. 1, no. 6, 2020. doi:10.1007/s42979-020-00365-y
[5] M. A. Khan, “An IOT framework for heart disease prediction based on MDCNN classifier,” IEEE Access, vol. 8, pp. 34717–34727, 2020. doi:10.1109/access.2020.2974687
[6] M. Tarawneh and O. Embarak, “Hybrid approach for heart disease prediction using data mining techniques,” Advances in Internet, Data and Web Technologies, pp. 447–454, 2019. doi:10.1007/978-3-030-12839-5_41
[7] N. Kagiyama, S. Shrestha, P. D. Farjo, and P. P. Sengupta, “Artificial Intelligence: Practical primer for clinical research in cardiovascular disease,” Journal of the American Heart Association, vol. 8, no. 17, 2019. doi:10.1161/jaha.119.012788
[8] N. L. Fitriyani, M. Syafrudin, G. Alfian, and J. Rhee, “HDPM: An effective heart disease prediction model for a clinical decision support system,” IEEE Access, vol. 8, pp. 133034–133050, 2020. doi:10.1109/access.2020.30105
[9] R. Indrakumari, T. Poongodi, and S. R. Jena, “Heart disease prediction using exploratory data analysis,” Procedia Computer Science, vol. 173, pp. 130–139, 2020. doi:10.1016/j.procs.2020.06.017
[10] S. Asadi, S. Roshan, and M. W. Kattan, “Random forest swarm optimization-based for heart diseases diagnosis,” Journal of Biomedical Informatics, vol. 115, p. 103690, 2021. doi:10.1016/j.jbi.2021.103690
[11] S. E. Ashri, M. M. El-Gayar, and E. M. El-Daydamony, “HDPF: Heart disease prediction framework based on hybrid classifiers and genetic algorithm,” IEEE Access, vol. 9, pp. 146797–146809, 2021. doi:10.1109/access.2021.3122789
[12] S. Mohan, C. Thirumalai, and G. Srivastava, “Effective heart disease prediction using hybrid machine learning techniques,” IEEE Access, vol. 7, pp. 81542–81554, 2019. doi:10.1109/access.2019.2923707
[13] T. G. Richardson et al., “Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable mendelian randomisation analysis,” PLOS Medicine, vol. 17, no. 3, 2020. doi:10.1371/journal.pmed.1003062
[14] U. Nagavelli, D. Samanta, and P. Chakraborty, “Machine learning technology-based heart disease detection models,” Journal of Healthcare Engineering, vol. 2022, pp. 1–9, 2022. doi:10.1155/2022/7351061
[15] V. Jackins, S. Vimal, M. Kaliappan, and M. Y. Lee, “AI-based smart prediction of clinical disease using random forest classifier and naive bayes,” The Journal of Supercomputing, vol. 77, no. 5, pp. 5198–5219, 2020. doi:10.1007/s11227-020-03481-x
[16] H. Santoso and A. Musdholifah, “Case base reasoning (CBR) and density based spatial clustering application with noise (DBSCAN)-based indexing in medical expert systems,” Khazanah Informatika?: Jurnal Ilmu Komputer dan Informatika, vol. 5, no. 2, pp. 169–178, 2019. doi:10.23917/khif.v5i2.8323
[17] Y. A. Nanehkaran et al., “Anomaly detection in heart disease using a density-based unsupervised approach,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1–14, 2022. doi:10.1155/2022/6913043
[18] S. Kannan, “Modelling an efficient clinical decision support system for heart disease prediction using learning and optimization approaches,” Computer Modeling in Engineering & Sciences, vol. 131, no. 2, pp. 677–694, 2022. doi:10.32604/cmes.2022.018580

Sayan Chakraborty, Trisha Mondal, Sayantan Maity, Saikat Pahari, "Heart Diseases Prediction Model Using Density Based Clustering", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.296-300, 2023.

The advent of smart homes using IoT has transformed the way households operate, providing convenience and efficiency to residents. However, the integration of IoT devices in the home network also creates security risks that may compromise the privacy and safety of individuals and households. This research paper provides an analysis of the security risks associated with smart homes using IoT, including hacking, unauthorized access, data breaches, device vulnerabilities, and insecure networks. The study also discusses the impact of these security risks on households and individuals, such as financial losses, privacy violations, physical harm, and emotional distress. The research design utilized a qualitative approach, including a literature review and interviews with experts in the field. The findings of the research emphasize the need for implementing strong security measures, such as strong passwords, two-factor authentication, encryption, and regular software updates, to mitigate the security risks associated with smart homes using IoT.

Smart homes, IoT, Security risks, Hacking, Data breaches, Privacy violations.

[1]. Alrawais, A., & Alenezi, A. (2020). Security challenges and solutions in smart homes: A survey. IEEE Access, 8, pp.158026-158045, 2020.
[2]. Aung, M. M., & He, W. (2019). Security and privacy in smart homes: A survey. IEEE Communications Surveys & Tutorials, 21(4), pp.2971-2998, (2019.
[3]. Chow, R., & Golle, P. (2014). Security and privacy challenges in the internet of things. IEEE Security & Privacy, 12(2), pp.102-114, 2014.
[4]. Das, S., & Mukhopadhyay, S. K. (2016). Security and privacy issues in smart homes: A survey. IEEE Communications Surveys & Tutorials, 18(4), pp.2296-2327, 2016.
[5]. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), pp.1645-1660, 2013.
[6]. Anderson, J., Wilson, R., & Smith, M. (2017). The Mirai Botnet attack: Analysis and implications. Journal of Cybersecurity, 10(2), 45-60.
[7]. Brown, K., & Davis, L. (2020). Enhancing IoT security: Industry-wide standards and best practices. International Journal of Information Security, 15(3), pp.167-182, 2020.
[8]. Davis, L. (2019). Recruitment methods for research participants: A systematic review. Journal of Research Methods, 25(4), pp.89-105, 2019.
[9]. Johnson, R., & Smith, A. (2018). Ethical guidelines for research with human subjects. Journal of Applied Ethics, 12(1), pp.35-50, 2018.
[10]. Jones, S., & Brown, K. (2020). Informed consent in research: Best practices and challenges. Journal of Ethics in Research, 18(2), pp.75-92, 2020.
[11]. Miller, P. (2018). Online advertising as a recruitment method in research: A comparative analysis. Journal of Research Methods, 22(3), pp.123-138, 2018.
[12]. Robinson, E. (2017). Security breaches in smart homes: Case studies and lessons learned. Journal of Cybersecurity Research, 5(1), pp.20-35, 2017.
[13]. Internet of Things Agenda (2020). IoT device security: An introduction.
[14]. McAfee. (n.d.). McAfee Labs Threats Report: March 2018.

Souvik Sikdar, Samya Das, Soham Dey, Radha Krishna Jana, "IoT Home Guard: Enhancing Security for Smart Home Privacy and Protection", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.301-309, 2023.

This research provides a cutting-edge method for remote and border surveillance utilizing a Using the most latest 3G technology, a multipurpose robot is used in military and defence applications. This autonomous car is capable of providing border area surveillance in place of soldiers. The robotic car uses the internet as a communication channel and may be operated manually or autonomously. This multimodal robot is employed in isolated and combat zones to find people, bombs, dangerous chemicals, and fire. Due to their finite range of frequency and restricted control which is manual, wireless security robots are traditionally obsolete. It is operating on one`s own and is managed by infrared and ultrasonic sensors. Cell phones are employed as video cameras by initiating a 3G video calls, and its operation is controlled by DTMF decoders. The robot`s path is changed based on real-time information about its surroundings. The experimental findings of the choice of tilt angle and power consumption of solar panels in automated and its manual modes are also illustrated in this study. Under specific conditions, this robotic vehicle is intended for both reconnaissance and surveillance.

Wireless Sensor Networks (WSN); DTMF; Sensors; PIR; GSM; GPS; Arduino Uno

[1] Tarunpreet kaur et al, Studied a designed small scale multipurpose military robot in manual controlled mode, the user may manually operate this security robot using a mobile phone to emit dual tone frequency that a DTMF decoder can detect.
[2] M. Mobarak Hossain, et al Both are studied on military robot and also given some features on their designed robot.
[3] Dr. S. Bhargavi etal has researched an intelligent combat robot designed specifically for war field where protection has been provided from enemies. It shows whenever enemies appear in front of the robot it will fire the laser gun.
[4] Multi-Purpose Robot Pallavi A. Jagtap, Mr P. R. Thorat e-ISSN: 2395 -0056, p-ISSN: 2395- 0072, Volume: 03, Issue:11, Nov -2016.
[5] Advanced Robotics and Humanoid Robots Aditi Pansari, Akrati Singh, Anoushka PathakIJIACS ISSN 2347 – 8616 Volume 7, Issue 2 February 2018.

Sushmitha Deb, Mandara V., Shashank P., "Multifunctional Smart Military Robot with Visual Inspection Robot - “Warrior Robo”", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.310-315, 2023.

Annually, over 1.15 million individuals are diagnosed with breast cancer across the globe. Today, only a select few accurate prognostic and predictive indicators are utilized in the clinical management of these patients. Early recognition of this deadly disease is pivotal, as it can reduce mortality rates while enhancing the life expectancy of those affected by breast cancer. Women are severely impacted by this condition, which has a high incidence and lethality rate. The absence of sturdy prognosis models complicates the medical professional`s ability to develop a treatment strategy that could extend the patient`s lifespan. Consequently, the need of the hour is to devise techniques that minimize error to boost accuracy. This study contrasts four algorithms, that predict the outcome of breast cancer, utilizing diverse datasets. All trials are performed within a simulated environment and facilitated on the JUPYTER platform. The research objective is divided into three sectors. The first sector entails predicting cancer prior to diagnosis, the second involves forecasting diagnosis and therapy, and the third centres on outcome during treatment. The proposed initiative can be employed to predict the outcomes of various methods, with the appropriate techniques chosen based on necessity. This investigation is undertaken to predict accuracy. Future studies can focus on predicting other distinct parameters, and breast cancer research can be classified based on these other parameters.

Machine Learning, Identification of Breast Cancer, Attribute Isolation, PCA, LDA, Decision Tree

[1] The Massivebio, accessed 06 June 2022 .
[2] J. R. Quinlan, “Improved Use of Continuous Attributes in C4.5,” J. Artif. Intell. Res., vol. 4, pp. 77–
90, Mar. 1996.
[3] H. J. Hamilton, N. Shan, and N. Cercone, “RIAC: A Rule Induction Algorithm Based on Approximate Classiication,” 1996.
[4] S. Nayak and D. Gope, "Comparison of supervised learning algorithms for RF-based breast cancer detection," 2017 Computing and Electromagnetics International Workshop (CEM), Barcelona, 2017, pp.
[5] L Breiman, Machine Learning 45, 5-32 (2001).
[6] M. T. Ribeiro, S. Singh, and C. Guestrin, “Why should I trust you?: Explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, ACM, 2016, pp. 1135–1144.
[7] A. A. Freitas, “Comprehensible classification models: A position paper,” ACM SIGKDD explorations newsletter, vol. 15, no. 1, pp. 1–10, 2014.
[8] UCI Repository, Wisconsin Breast Cancer Database (WBCD), 2018

S Santra, A Sarkar, P Sahoo, T Ghosh, S Gautam, A Bhowmik,S Majumdar,A Bhowmik, S Bhowmik, P Sarkar, "Designing Interpretable and Transparent Machine Learning Models for Early Detection of Breast Cancer", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.316-322, 2023.

The primary objective of this undertaking is to acquire a comprehensive understanding of the health status of a designated region, be it a district, state, or even an entire country. The central methodology employed in this project involves the meticulous accumulation of significant health-related data, which is subsequently translated into visually informative displays. To facilitate this, an extensive survey was meticulously conducted among the populace of Assam, constituting a pivotal phase in the data acquisition process. The resultant data corpus was then meticulously categorized according to their respective geographic subdivisions. Within the realm of this analysis, particular focus was directed towards three prevalent ailments: asthma, chronic illnesses, and arthritis. These health conditions were chosen as the focal points due to their widespread impact on the populace. To effectively convey the prevalence of each malady, a standardized metric was employed wherein the frequency of cases was represented relative to a population of one lakh. The utilization of graphical representations serves as a potent tool in conveying intricate health-related insights to a broader audience. By visually portraying the prevalence of key diseases across various districts, a clearer understanding of the health landscape emerges. This comprehensive approach to data collec- tion and visualization is pivotal in facilitating informed decision- making processes for healthcare providers, policymakers, and stakeholders alike.

Big Data, Health care, Health Tracking System.

[1]Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation, Samuel L. Groseclose1 and David L. Buckeridge2.
[2]Implications of big data analytics in developing healthcare frameworks – A review Venketesh Palanisamy, Ramkumar Thirunavukarasu
[3]Leveraging big data in population health management, Timothy S. Wells1*, Ronald J. Ozminkowski2, Kevin Hawkins1, Gandhi R. Bhattarai3 and Douglas G. Armstrong4
[4]Big data handling mechanisms in the healthcare applications: A comprehensive and systematic literature review, Asma Pashazadeh, Nima Jafari Navimipour
[5]Methodologies for designing healthcare analytics solutions: A literature analysis, Shah J Miah, John Gammack, Najmul Hasan
[6]Leveraging big data in population health management Timothy S. Wells1*, Ronald J. Ozminkowski2, Kevin Hawkins1, Gandhi R. Bhattarai3 and Douglas G. Armstrong4

Aneesh Bose, Deblina Pal, Radhakrishna Jana, "Population Health Tracking System: Analyzing Health Data for Effective Healthcare Planning", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.323-330, 2023.

Machine Learning has become a pervasive technology, finding applications in diverse fields across the globe, including the healthcare industry. This transformative technology has the potential to significantly impact medical diagnostics and predictive analysis, aiding in the early detection of various conditions such as locomotor disorders, heart diseases, and many others. By accurately predicting the presence or absence of these ailments in advance, valuable insights can be provided to medical professionals, empowering them to personalize their diagnoses and treatment plans on a patient-by-patient basis, thus revolutionizing the medical field. In this paper, our primary focus lies in predicting possible heart diseases using cutting-edge Machine Learning algorithms. By leveraging the power of these algorithms, we aim to facilitate a comparative analysis of classifiers, including decision tree, K-Nearest Neighbours, Logistic Regression, Support Vector Machine, and Random Forest. Through this analysis, we seek to identify the most suitable classifier that yields the most accurate results for heart disease prediction.

Confusion Matrix, K-Nearest Neighbours, Dummies.

[1] Saxena, Kanak, and Richa Sharma. "Efficient heart disease prediction system using decision tree." In International Conference on Computing, Communication & Automation, pp.72-77, 2015, IEEE.
[2] Chauhan, Aakash, Aditya Jain, Purushottam Sharma, and Vikas Deep. "Heart disease prediction using evolutionary rule learning." In 2018 4th International conference on computational intelligence & communication technology (CICT), pp. 1-4, 2018, IEEE.
[3] Prof. Sachin Sambhaji Patil, Vaibhavi Dhumal, Srushti Gavale, Himanshu Kulkarni, Shreyash Wadmalwar, " Heart Disease Prediction using Machine Learning", International Journal of Scientific Research in Science and Technology (IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Vol.9, Issue.6, pp.541-546, 2022.
[4] Lakshmanarao, A., Y. Swathi, and P. Sri Sai Sundareswar. "Machine learning techniques for heart disease prediction." Forest 95, no.99: 97, 2019.
[5] Avinash Golande, Pavan Kumar T proposed “Heart Disease Prediction Using Effective Machine Learning Techniques”. International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Vol.8, Issue.1S4, 2019.
[6] Deepika, Kumari, and S. Seema. "Predictive analytics to prevent and control chronic diseases." In 2016 2nd international conference on applied and theoretical computing and communication technology (iCATccT), pp. 381-386, 2016, IEEE.
[7] Mohan, Senthilkumar, Chandrasegar Thirumalai, and Gautam Srivastava. "Effective heart disease prediction using hybrid machine learning techniques." IEEE access 7: pp.81542-81554, 2019.
[8] Rahman, Md Mahbubur, Morshedur Rahman Rana, M. N. A. Alam, Md Saikat Islam Khan, and Khandaker Mohammad Mohi Uddin. "A web-based heart disease prediction system using machine learning algorithms." Network Biology 12, no.2: 64-80, 2022.
[9] Khan, Arsalan, Moiz Qureshi, Muhammad Daniyal, and Kassim Tawiah. "A Novel Study on Machine Learning Algorithm-Based Cardiovascular Disease Prediction." Health & Social Care in the Community 2023 (2023).
[10] Riyaz, Lubna, Muheet Ahmed Butt, Majid Zaman, and Omeera Ayob. "Heart disease prediction using machine learning techniques: a quantitative review." In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2021, Volume 3, pp. 81-94. Springer Singapore, 2022.
[11] Sun, Huating, and Jianan Pan. "Heart Disease Prediction Using Machine Learning Algorithms with Self-Measurable Physical Condition Indicators." Journal of Data Analysis and Information Processing 11, no.1: pp.1-10, 2023.
[12] Boukhatem, Chaimaa, Heba Yahia Youssef, and Ali Bou Nassif. "Heart disease prediction using machine learning." In 2022 Advances in Science and Engineering Technology International Conferences (ASET), pp. 1-6, 2022, IEEE.
[13] Yadav, Anupama, Levish Gediya, and Adnanuddin Kazi. "Heart disease prediction using machine learning." International Research Journal of Engineering and Technology (IRJET 8, no. 09, 2021.
[14] Jindal, Harshit, Sarthak Agrawal, Rishabh Khera, Rachna Jain, and Preeti Nagrath. "Heart disease prediction using machine learning algorithms." In IOP conference series: materials science and engineering, vol. 1022, no. 1, p. 012072. IOP Publishing, 2021.
[15] Katarya, Rahul, and Sunit Kumar Meena. "Machine learning techniques for heart disease prediction: a comparative study and analysis." Health and Technology 11: pp.87-97, 2021.
[16] Ali, Md Mamun, Bikash Kumar Paul, Kawsar Ahmed, Francis M. Bui, Julian MW Quinn, and Mohammad Ali Moni. "Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison." Computers in Biology and Medicine 136 (2021): 104672.
[17] Patel, Jaydutt, Azhar Ali Khaked, Jitali Patel, and Jigna Patel. "Heart disease prediction using machine learning." In Proceedings of Second International Conference on Computing, Communications, and Cyber-Security: IC4S 2020, pp. 653-665. Springer Singapore, 2021.

Ira Nath, Sk Md Toueb Rahaman, Arnab Ghosh, Suvajit Paul, Tathagata Gupta, Dharmpal Singh, "NTPHD: A Novel Technique to Predict Heart Disease", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.331-336, 2023.