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i-manager's Journal on Software Engineering (JSE)

Current Issue Vol. 18 Issue 2

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Volume 15 Issue 3 January - March 2021

Article

A Review on Data Mining and Big Data Analytics for Healthcare Solutions

Hiralkumari B. Chauhan* , Rahul K. Kher **

* Department of Information Technology, Bhailalbhai and Bhikhabhai Institute of Technology, Vallabh Vidyanagar, Gujarat, India.

** Department of Electronics and Communication Engineering, G H Patel College of Engineering and Technology (GCET), Gujarat, India.

Chauhan, H. B., and Kher, R. K. (2021). A Review on Data Mining and Big Data Analytics for Healthcare Solutions. i-manager's Journal on Software Engineering, 15(3), 1-4. https://doi.org/10.26634/jse.15.3.18254

Abstract

A personalized health care system model is proposed that provides e-health services in term of prediction, disease diagnosis and prevention strategy. Medical Internet of Things (MIoT) is utilized for generating various smart devices, sensors and mobile application. The collected data from hospitals, clinics, and laboratories will be stored on the cloud with the help of cloud and fog computing. Big data analytics and data mining algorithms can provide real time data analysis, testing and decision making. The required percussions will suggest an emergency service to user, if needed.

References

[1]. Abidi, S. S. R., & Abidi, S. R. (2019). Intelligent health data analytics: A convergence of artificial intelligence and big data. Healthcare Management Forum, 32(4). https:// doi.org/10.1177/0840470419846134

[2]. Hewner, S., Sullivan, S. S., & Yu, G. (2018). Reducing emergency room visits and in‐hospitalizations by implementing best practice for transitional care using innovative technology and big data. Worldviews on Evidence‐Based Nursing, 15(3), 170-177. https://doi.org/ 10.1111/wvn.12286

[3]. Kher, R., Pawar, T., Thakar, V., & Shah, H. (2015). Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines. Journal of Medical Engineering and Technology, 39(2), 138–152. https://doi.org/10.3109/03091902.2014.998372

[4]. Kumari, S. S., & Rani, K. S. (2018). Big data analytics for healthcare system. SSRN Electronic Journal. https://doi. org/10.2139/ssrn.3168338

[5]. Nanayakkara, S., Zhou, X., & Spallek, H. (2019). Impact of big data on oral health outcomes. Oral Diseases, 25(5), 1245-1252. https://doi.org/10.1111/odi. 13007

[6]. Sanchez-Pinto, L. N., Luo, Y., & Churpek, M. M. (2018). Big data and data science in critical care. Chest, 154(5), 1239-1248. https://doi.org/10.1016/j.chest.2018.04.037

[7]. Schlick, C. J. R., Castle, J. P., & Bentrem, D. J. (2018). Utilizing big data in cancer care. Surgical Oncology Clinics, 27(4), 641-652. https://doi.org/10.1016/j.soc.2018. 05.005

[8]. Song, C. W., Jung, H., & Chung, K. (2019). Development of a medical big-data mining process using topic modeling. Cluster Computing, 22(1), 1949-1958. https://doi.org/10.1007/s10586-017-0942-0

[9]. Wang, Y., Kung, L., Wang, W. Y. C., & Cegielski, C. G. (2018). An integrated big data analytics-enabled transformation model: Application to health care. Information & Management, 55(1), 64-79. https://doi.org/ 10.1016/j.im.2017.04.001

[10]. Wordsworth, S., Doble, B., Payne, K., Buchanan, J., Marshall, D. A., McCabe, C., & Regier, D. A. (2018). Using “big data” in the cost-effectiveness analysis of nextgeneration sequencing technologies: challenges and potential solutions. Value in Health, 21(9), 1048-1053. https://doi.org/10.1016/j.jval.2018.11.006

[11]. Zhao, Y., Fang, L., Cui, L., & Bai, S. (2020). Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery. BMC Medical Informatics and Decision Making, 20(1), 1-13. https://doi. org/10.1186/s12911-020-01180-4

[12]. Zheng, Y., & Chen, G. (2019). Energy analysis and application of data mining algorithms for internet of things based on Hadoop cloud platform. IEEE Access, 7, 183195–183206. https://doi.org/10.1109/ACCESS.2019.2 958377

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Research Paper

Identification of the Types of Skin Cancers from Skin Cancer Images and Covid-19 Detection on Chest X-Ray Images using Deep Learning

Debabrat Bharali* , Wahengbam Shibananda Singh , Kuldeep Nath, Afrin Haque , Dipu Medhi

*-***** Department of Computer Science, Regional Institute of Science and Technology, Meghalaya, India.

Bharali, D., Singh, W. S., Nath, K., Haque, A., and Medhi, D. (2021). Identification of the Types of Skin Cancers from Skin Cancer Images and Covid-19 Detection on Chest X-Ray Images using Deep Learning. i-manager's Journal on Software Engineering, 15(3), 5-20. https://doi.org/10.26634/jse.15.3.18329

World Health Organization : COVID-19 - Global literature on coronavirus disease

https://pesquisa.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/en/covidwho-1630418

ProQuest Central | ID: covidwho-1630418

Abstract

COVID-19 is a very deadly disease, which has killed thousands and infected millions of people worldwide. More recently in the year 2021, one of its mutants known as "The Delta Variant" has ravaged our country. It is also currently the chief cause of increasing cases in some North-Eastern states like Manipur and Arunachal Pradesh. Different measures have been adopted by the Government in collaboration with local social bodies to identify the infected individuals, detect the level of infection and also vaccinating individuals to shield them from this deadly disease. The current paper is also focused on one such stage, which is quite critical at this juncture, and will use the power of Artificial Intelligence to appropriately identify COVID-19 affected individuals using chest X-Ray images. When implemented, it will make it easier to identify the infection of the lungs by COVID-19. More specifically, the proposed methodology seeks to establish a chain of processes that can help in detecting the infection in the lungs using an advanced and novel image pre-processing with a prediction fusion-based deep learning-based identification system. The image pre-processing technique will initially improve the raw images by selectively optimizing the chromatic intensity and brightness of needy pixels using a deep learning-based Conditional Random Field (CRF) that uses the sigmoidal function. The enhanced image samples are made to undergo training with GoogLeNet and MobileNet deep learning models so that during the testing phase a prediction-fusion approach can be implemented to generate more robust prediction results. An exhaustive implementation with a standard dataset has revealed that the proposed approach can provide a mean accuracy of 98.63%, with the Covid and Normal classes showing 97.17% and 99.22% accuracies respectively. Another deadly disease that has infected thousands of people worldwide is skin cancer. Using the similar technical approach described above, a technique for identifying the type of skin cancer has been developed and experimented by using a standard dataset. Good accuracy of 85.42% has been achieved despite some classes having a comparatively lesser number of image samples. Finally, a Graphical User Interface (GUI) has also been developed by using the trained deep learning files of GoogLeNet and MobileNet so that a user can simply enter the desired image and check the type of prediction/class.

References

[1]. Abuzaghleh, O., Barkana, B. D., & Faezipour, M. (2015). Noninvasive real-time automated skin lesion analysis system for melanoma early detection and prevention. IEEE Journal of Translational Engineering in Health and Medicine, 3, 1-12. https://doi.org/10.1109/JTE HM.2015.2419612

[2]. Aggarwal, A., Das, N., & Sreedevi, I. (2019, November). Attention-guided deep convolutional neural networks for skin cancer classification. In 2019, Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA) (pp. 1-6). IEEE. https://doi.org/ 10.1109/IPTA.2019.8936100

[3]. Al-Bawi, A., Al-Kaabi, K., Jeryo, M., & Al-Fatlawi, A. (2020). CCBlock: An effective use of deep learning for automatic diagnosis of COVID-19 using X-ray images. Research on Biomedical Engineering, 1-10. https://doi. org/10.1007/s42600-020-00110-7

[4]. Alquran, H., Qasmieh, I. A., Alqudah, A. M., Alhammouri, S., Alawneh, E., Abughazaleh, A., & Hasayen, F. (2017, October). The melanoma skin cancer detection and classification using support vector machine. In 2017, IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) (pp. 1-5). IEEE. https://doi.org/10.1109/AEECT. 2017.8257738

[5]. Asif, S., Wenhui, Y., Jin, H., & Jinhai, S. (2020, December). Classification of COVID-19 from chest X-ray images using Deep Convolutional Neural Network. In 2020, IEEE 6th International Conference on Computer and Communications (ICCC) (pp. 426-433). IEEE. https://doi. org/10.1109/ICCC51575.2020.9344870

[6]. Bassi, P. R., & Attux, R. (2021). A deep convolutional neural network for COVID-19 detection using chest X-rays. Research on Biomedical Engineering, 1-10. https://doi. org/10.1007/s42600-021-00132-9

[7]. Capdehourat, G., Corez, A., Bazzano, A., Alonso, R., & Musé, P. (2011). Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions. Pattern Recognition Letters, 32(16), 2187-2196. https://doi.org/ 10.1016/j.patrec.2011.06.015

[8]. Cohen, J. P., Morrison, P., & Dao, L. (2020). COVID-19 image data collection. ArXiv Preprint. Retrieved from https://arxiv.org/pdf/2003.11597.pdf

[9]. Demir, A., Yilmaz, F., & Kose, O. (2019, October). Early detection of skin cancer using deep learning architectures: Resnet-101 and Inception-v3. In 2019, Medical Technologies Congress (TIPTEKNO) (pp. 1-4). IEEE. https://doi.org/10.1109/TIPTEKNO47231.2019.8972045

[10]. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. ArXiv Preprint. Retrieved from https://arxiv.org/ abs/1704.04861

[11]. Jain, S., & Pise, N. (2015). Computer aided melanoma skin cancer detection using image processing. Procedia Computer Science, 48, 735-740. https://doi.org/10.1016/j.procs.2015.04.209

[12]. Keidar, D., Yaron, D., Goldstein, E., Shachar, Y., Blass, A., Charbinsky, L., … Eldar, Y. C. (2021). COVID-19 classification of X-ray images using deep neural networks. European Radiology, 1-10. https://doi.org/10.1007/s0033 0-021-08050-1

[13]. Mane, S. S., & Shinde, S. V. (2017). Different techniques for skin cancer detection using dermoscopy images. International Journal of Computer Sciences and Engineering, 5(12), 165-170.

[14]. Mangal, A., Kalia, S., Rajgopal, H., Rangarajan, K., Namboodiri, V., Banerjee, S., & Arora, C. (2020). COVIDAID: COVID-19 detection using chest X-Ray. ArXiv Preprint. Retrieved from https://arxiv.org/abs/2004.09803

[15]. Moldovan, D. (2019, November). Transfer learning based method for two-step skin cancer images classification. In 2019, E-Health and Bioengineering Conference (EHB) (pp. 1-4). IEEE. https://doi.org/10.1109/ EHB47216.2019.8970067

[16]. Sekeroglu, B., & Ozsahin, I. (2020). Detection of COVID-19 from chest X-ray images using Convolutional Neural Networks. SLAS Technology: Translating Life Sciences Innovation, 25(6), 553-565. https://doi.org/10.11 77%2F2472630320958376

[17]. Soumya, R. S., Neethu, S., Niju, T. S., Renjini, A., & Aneesh, R. P. (2016, July). Advanced earlier melanoma detection algorithm using colour correlogram. In 2016, International Conference on Communication Systems and Networks (ComNet) (pp. 190-194). IEEE. https://doi. org/10.1109/CSN.2016.7824012

[18]. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-9). https:// doi.org/10.1109/CVPR.2015.7298594

[19]. Tschandl, P., Rosendahl, C., & Kittler, H. (2018). The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data, 5(1), 1-9. https://doi.org/10.1038/sdata.2018.161

[20]. Wang, L., Lin, Z. Q., & Wong, A. (2020). COVID-net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Scientific Reports, 10(1), 1-12. https://doi.org/10.1038/s41 598-020-76550-z

[21]. Yadav, S., Sandhu, J. K., Pathak, Y., & Jadhav, S. (2020). Chest X-ray scanning based detection of COVID- 19 using deep convolutional neural network. Retrieved from https://assets.researchsquare.com/files/rs-58833/v1_ stamped.pdf?c=1597440025

[22]. Younis, H., Bhatti, M. H., & Azeem, M. (2019, December). Classification of skin cancer dermoscopy images using transfer learning. In 2019, 15th International Conference on Emerging Technologies (ICET) (pp. 1-4). IEEE. https://doi.org/10.1109/ICET48972.2019.8994508

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Research Paper

An Effective Model for Choosing Career in Schools using Data Mining Techniques

R. Thirumalaiselvi * , P. Narayanan**

*-** Department of Computer Science, Government Arts College for Men, Chennai, Tamil Nadu, India.

Thirumalaiselvi, R., and Narayanan, P. (2021). An Effective Model for Choosing Career in Schools using Data Mining Techniques. i-manager's Journal on Software Engineering, 15(3), 21-28. https://doi.org/10.26634/jse.15.3.18319

Abstract

An increasing number of future labour force will not just comprise highly skilled human resources, but would also seek to hire personnel with sound technical, analytical, and soft skills to get engaged in cross-cultural and multi-lingual organizational setup. The major challenge of higher education is the lack of knowledge of the talents of students, so their chances of success decreases. Choosing a wrong course leads to incompletion and getting a job becomes tougher. Predicting student skills early can help mentors to advice students in a timely manner and improve student success. In this paper, a model has been created using Naïve Bayes, J48, Random Forest, and Support Vector Machine (SVM) classification algorithm, with 100 attributes. Among the models built, Naïve Bayes and Random Forest algorithm yielded better accuracy rating. In this research work, we attempt to explore dynamic dataset by applying data mining methods to explore student's insights based on characteristics related to academic, technical, environment and interpersonal factors. The model has been tested and found to be performing well in constraint based learning environment.

References

[1]. Agaoglu, M. (2016). Predicting instructor performance using data mining techniques in higher education. IEEE Access, 4, 2379-2387. https://doi.org/10.1109/ACCESS.20 16.2568756

[2]. Arafath, M. Y., Saifuzzaman, M., Ahmed, S., & Hossain, S. A. (2018, September). Predicting career using data mining. In 2018, International Conference on Computing, Power and Communication Technologies (GUCON) (pp. 889-894). IEEE. https://doi.org/10.1109/GUCON.2018.867 4995

[3]. Aziz, S. M., & Awlla, A. H. (2019). Performance analysis and prediction student performance to build effective student using data mining techniques. UHD Journal of Science and Technology, 3(2), 10-15.

[4]. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510

[5]. Denila, P. G., Delima, A. J. P., & Vilchez, R. N. (2020). Analysis of IT graduates employment alignment using C4. 5 and Naïve Bayes algorithm. International Journal of Advanced Trends in Computer Science and Engineering, 9, 745-752.

[6]. Girase, M., Lad, S., &Pachpande, P. (2018). Student's employability prediction using data mining. International Journal of Scientific and Engineering Research, 9(4), 27- 29.

[7]. Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K. U., & Sattar, M. U. (2020). Predicting student performance in higher educational institutions using video learning analytics and data mining techniques. Applied Sciences, 10(11), 3894. https://doi.org/10.3390/app1011 3894

[8]. Hussain, S., Dahan, N. A., Ba-Alwib, F. M., & Ribata, N. (2018). Educational data mining and analysis of students' academic performance using WEKA. Indonesian Journal of Electrical Engineering and Computer Science, 9(2), 447-459. https://doi.org/10.11591/ijeecs.v9.i2

[9]. Jain, D., Yadav, A., Yadav, C., Ramrakhyani, D., Shah, F., & Jain, M. (2020). Career prediction system. International Research Journal of Modernization in Engineering Technology and Science, 2(4), 1050-1052.

[10]. Jamil, A., Ahsan, M., Farooq, T., Hussain, A., & Ashraf, R. (2018, September). Student performance prediction using algorithms of data mining. In 2018, International Conference on Computing, Engineering, and Design (ICCED) (pp. 244-249). IEEE. https://doi.org/10.1109/ICC ED.2018.00055

[11]. Kabakchieva, D. (2013). Predicting student performance by using data mining methods for classification. Cybernetics and Information Technologies, 13(1), 61-72. https://doi.org/10.2478/cait-2013-0006

[12]. Mueen, A., Zafar, B., & Manzoor, U. (2016). Modeling and predicting students' academic performance using data mining techniques. International Journal of Modern Education and Computer Science, 8(11), 36-42. https:// doi.org/10.5815/ijmecs.2016.11.05

[13]. Premalatha, N., & Sujatha, S. (2019). A comparative study on placement performance using data mining algorithms in International Journal of Science and Technology in Research, 8(10), 1806-1812.

[14]. Repaso, J. A. A., & Capariño, E. T. (2020). Analyzing and predicting career specialization using classification techniques. International Journal of Advanced Trends in Computer Science and Engineering, 9(1.3), 342-348. https://doi.org/10.30534/ijatcse/2020/5391.32020

[15]. Vijayalkshmi, B. M., & Ananthanarayanan, N. R. (2018). Analysis of student performance in govt. girl higher secondary school using data mining techniques. International Journal of Creative Research Thoughts, 6(1), 1514-1521.

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Research Paper

Classification of the DDoS Attack over Flash Crowd with DNN using World Cup 1998 and CAIDA 2007 Datasets

Ch. Sekhar* , K. Venkata Rao , M. H. M. Krishna Prasad *

, Department of Computer Science and Engineering, JNTU Kakinada, Andhra Pradesh, India.

Department of Computer Science and Engineering, Vignan's IIT Kakinada, Andhra Pradesh, India.

Sekhar, C., Rao, K. V., and Prasad, M. H. M. K. (2021). Classification of the DDoS Attack over Flash Crowd with DNN using World Cup 1998 and CAIDA 2007 Datasets. i-manager's Journal on Software Engineering, 15(3), 29-36. https://doi.org/10.26634/jse.15.3.18353

Abstract

Present day's e-commerce business has tremendously increased as everyone got Internet on their hands through their mobile devices. E-commerce big giants like Amazon, Alibaba, Flipkart, etc. have come up with surprise sales with huge discounts on the products called Flash Events (FE) or Flash Sales (FS). It attracts the customers to purchase the product on such specified dates. Huge client requests were coming into the servers on these days. Based on this scenario, attackers target these networks to degrade the performance of e-commerce portals by generating huge fake server requests called Distributed Denial of Service (DDoS) attacks. Network attacks caused during Flash Events (FE), Flash Sales (FS) are considered as Flash Crowd attacks (FC). With FC attacks, the performance of the server is reduced as well as it affects the clients by not sending proper responses. In this paper, the two datasets to CAIDA and WC 1998 datasets have been considered. WC 1998 dataset deals with flash crowd and CAIDA dataset have DDoS attack information. Similar features from both datasets have been taken and the flash crowd and DDoS attacks have been classified using the Deep Neural Network (DNN) approach. The accuracy of discriminating the DDoS and FC/FE with an accuracy of 70.49 % at 100 epochs and 72.1 % at 1000 epochs has been achieved.

References

[1]. Akilandeswari, V., & Shalinie, S. M. (2012, December). Probabilistic neural network based attack traffic classification. In 2012, Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-8). IEEE. https://doi. org/10.1109/ICoAC.2012.6416848

[2]. Center for Applied Internet Data Analysis. (2007). The CAIDA "DDoS Attack 2007" Dataset. Retrieved from https:// www.caida.org/catalog/datasets/ddos-20070804_ dataset/

[3]. Chevalier, S. (2020). Global retail e-commerce sales worldwide from 2014-2024. Retrieved from https://www. statista.com/statistics/379046/worldwide-retail-ecommerce- sales/

[4]. Felter, B. (2020). 7 of the most famous recent DDoS attacks. Retrieved from https://www.vxchnge.com/blog/ recent-ddos-attacks-on-companies

[5]. Infographic. (n.d.). Impact of DDoS on enterprise organizations, Corero. Retrieved from https://www.corero. com/blog/infographic-impact-of-ddos-on-enterpriseorganizations/

[6]. Kandula, S., Katabi, D., Jacob, M., & Berger, A. (2005, May). Surviving organized DDoS attacks that mimic flash crowds. In USENIX 2nd Symposium on Network System Design and Implementation (pp. 287-300).

[7]. Le, Q., Zhanikeev, M., & Tanaka, Y. (2007, May). Methods of distinguishing flash crowds from spoofed DoS attacks. In 2007, Next Generation Internet Networks (pp. 167-173). IEEE. https://doi.org/10.1109/NGI.2007.371212

[8]. Office Chai. (2015). The story behind the site's big billion days crash in 2015. Retrieved from https://officechai. com/startups/former-flipkart-cpo-punit-soni-reveals-storybehind- sites-big-billion-days-crash-2015/

[9]. Sekhar, C. (2021). Deep learning algorithms for intrusion detection systems: Extensive comparison analysis. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(11), 2990-3000.

[10]. Sekhar, C. H., & Rao, K. V. (2019, May). A study: Machine learning and deep learning approaches for intrusion detection system. In International Conference on Computer Networks and Inventive Communication Technologies (pp. 845-849). Cham: Springer. https://doi. org/10.1007/978-3-030-37051-0_94

[11]. Sekhar, Ch., Rao, K. V., & Prasad, M. H. M. K. (2020). Comparison performance of machine learning techniques for intrusion detection system: A review. i-manager's Journal on Computer Science, 7(4), 55-61. https://doi.org/10.26634/ jcom.7.4.17108

[12]. Sujata, J., Menachem, D., Akshay, P., & Viraj, T. (2017). Impact of flash sales on consumers & e-commerce industry in India. In Annual International Conference on Qualitative & Quantitative Economics Research (pp. 11- 19). https://doi.org/10.5176/2251-2012_QQE17.9

[13]. Tandon, R., Palia, A., Ramani, J., Paulsen, B., Bartlett, G., & Mirkovic, J. (2021, June). Defending web servers against flash crowd attacks. In International Conference on Applied Cryptography and Network Security (pp. 338- 361). Cham: Springer. https://doi.org/10.1007/978-3-030- 78375-4_14

[14]. World Cup. (1998). Git Hub Gist. Retrieved from https://gist.github.com/Mic92/633676c3d4456b1371e07 2dee7d4650e

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Research Paper

Parkinson’s Disease Prediction using Machine Learning

Preethi S.* , Priyadharshini K. S. , Kavitha A. *

*-*** Department of Information Technology, Bannari Amman Institute of Technology, Erode, Tamil Nadu, India.

Preethi, S., Priyadharshini, K. S., and Kavitha, A. (2021). Parkinson's Disease Prediction using Machine Learning. i-manager's Journal on Software Engineering, 15(3), 37-41. https://doi.org/10.26634/jse.15.3.18133

Abstract

Parkinson's disease is a disorder which is identified with loss of neurons and neurologic function. It is a condition that arises when fifty to seventy five percent of the neuronal cells are affected. The symptoms include muscle rigidity, tremors and change in the speech and gait. The genetic factor also increases the risk of Parkinson's disease in a person. Some researchers also suggest that Parkinson's disease is also caused by environmental factors and excessive medications. With the advancement of deep learning and machine learning technologies, disease prediction has received additional attention from big data researchers, and numerous studies have been conducted with a choice of different mechanisms. Studies have shown that about 90% of patients with this disease suffer from certain degree of speech impairment. Therefore, we have chosen voice data as an input for our model. The proposed methodology presents how algorithm works best for identification of disease with high accuracy by splitting the dataset. XGBoost algorithm has been applied on the dataset in order to get accuracy expected out of the model.

References

[1]. Abdulhay, E., Arunkumar, N., Narasimhan, K., Vellaiappan, E., & Venkatraman, V. (2018). Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease. Future Generation Computer Systems, 83, 366-373. https://doi.org/10.1016/j. future.2018.02.009

[2]. Abiyev, R. H., & Abizade, S. (2016). Diagnosing Parkinson's diseases using fuzzy neural system. Computational and Mathematical Methods in Medicine. 2016, Article ID 1267919. https://doi.org/10.1155/2016/ 1267919

[3]. Choi, H., Ha, S., Im, H. J., Paek, S. H., & Lee, D. S. (2017). Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging. Neuro Image: Clinical, 16, 586-594. https://doi. org/10.1016/j.nicl.2017.09.010

[4]. El Moudden, I., Ouzir, M., & ElBernoussi, S. (2017, February). Automatic speech analysis in patients with Parkinson's disease using feature dimension reduction. In Proceedings of the 3rd International Conference on Mechatronics and Robotics Engineering (pp. 167-171). https://doi.org/10.1145/3068796.3068813

[5]. Oh, S. L., Hagiwara, Y., Raghavendra, U., Yuvaraj, R., Arunkumar, N., Murugappan, M., & Acharya, U. R. (2020). A deep learning approach for Parkinson's disease diagnosis from EEG signals. Neural Computing and Applications, 32(15), 10927-10933. https://doi.org/10.100 7/s00521-018-3689-5

[6]. Sahyoun, A., Chehab, K., Al-Madani, O., Aloul, F., & Sagahyroon, A. (2016, September). ParkNosis: Diagnosing Parkinson's disease using mobile phones. In 2016, IEEE 18 International Conference on e-Health Networking, Applications and Services (Healthcom) (pp. 1-6). IEEE. https://doi.org/10.1109/HealthCom.2016.7749491

[7]. Seppi, K., Ray Chaudhuri, K., Coelho, M., Fox, S. H., Katzenschlager, R., Perez Lloret, S., Djamshidian-Tehrani, A. (2019). Update on treatments for nonmotor symptoms of Parkinson's disease—An evidence‐based medicine review. Movement Disorders, 34(2), 180-198. https://doi. org/10.1002/mds.27602

[8]. Shetty, S., & Rao, Y. S. (2016, August). SVM based machine learning approach to identify Parkinson's disease using gait analysis. In 2016, International Conference on Inventive Computation Technologies (ICICT) (Vol. 2, pp. 1- 5). IEEE. https://doi.org/10.1109/INVENTIVE.2016.7824836

[9]. Subramanian, K., & Suresh, S. (2012). Human action recognition using meta-cognitive neuro-fuzzy inference system. International Journal of Neural Systems, 22(06). https://doi.org/10.1142/S0129065712500281

[10]. Tayal, A. (2018, October). Determination of Parkinson's disease utilizing machine learning methods. In 2018, International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) (pp. 170-173). IEEE. https://doi.org/10.1109/ ICACCCN.2018.8748662

[11]. Zhang, B., Ren, J., Cheng, Y., Wang, B., & Wei, Z. (2019). Health data driven on continuous blood pressure prediction based on gradient boosting decision tree algorithm. IEEE Access, 7, 32423-32433. https://doi.org/10. 1109/ACCESS.2019.2902217

i-manager's Journal on Software Engineering (JSE)

Current Issue Vol. 18 Issue 2

Most Read

Most Cited

Volume 15 Issue 3 January - March 2021

A Review on Data Mining and Big Data Analytics for Healthcare Solutions

Hiralkumari B. Chauhan* , Rahul K. Kher **

* Department of Information Technology, Bhailalbhai and Bhikhabhai Institute of Technology, Vallabh Vidyanagar, Gujarat, India. ** Department of Electronics and Communication Engineering, G H Patel College of Engineering and Technology (GCET), Gujarat, India.

Chauhan, H. B., and Kher, R. K. (2021). A Review on Data Mining and Big Data Analytics for Healthcare Solutions. i-manager's Journal on Software Engineering, 15(3), 1-4. https://doi.org/10.26634/jse.15.3.18254

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Identification of the Types of Skin Cancers from Skin Cancer Images and Covid-19 Detection on Chest X-Ray Images using Deep Learning

Debabrat Bharali* , Wahengbam Shibananda Singh **, Kuldeep Nath***, Afrin Haque ****, Dipu Medhi *****

*-***** Department of Computer Science, Regional Institute of Science and Technology, Meghalaya, India.

World Health Organization : COVID-19 - Global literature on coronavirus disease

ProQuest Central | ID: covidwho-1630418

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An Effective Model for Choosing Career in Schools using Data Mining Techniques

R. Thirumalaiselvi * , P. Narayanan**

*-** Department of Computer Science, Government Arts College for Men, Chennai, Tamil Nadu, India.

Thirumalaiselvi, R., and Narayanan, P. (2021). An Effective Model for Choosing Career in Schools using Data Mining Techniques. i-manager's Journal on Software Engineering, 15(3), 21-28. https://doi.org/10.26634/jse.15.3.18319

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Classification of the DDoS Attack over Flash Crowd with DNN using World Cup 1998 and CAIDA 2007 Datasets

Ch. Sekhar* , K. Venkata Rao **, M. H. M. Krishna Prasad ***

*,*** Department of Computer Science and Engineering, JNTU Kakinada, Andhra Pradesh, India. ** Department of Computer Science and Engineering, Vignan's IIT Kakinada, Andhra Pradesh, India.

Sekhar, C., Rao, K. V., and Prasad, M. H. M. K. (2021). Classification of the DDoS Attack over Flash Crowd with DNN using World Cup 1998 and CAIDA 2007 Datasets. i-manager's Journal on Software Engineering, 15(3), 29-36. https://doi.org/10.26634/jse.15.3.18353

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Parkinson’s Disease Prediction using Machine Learning

Preethi S.* , Priyadharshini K. S. **, Kavitha A. ***

*-*** Department of Information Technology, Bannari Amman Institute of Technology, Erode, Tamil Nadu, India.

Preethi, S., Priyadharshini, K. S., and Kavitha, A. (2021). Parkinson's Disease Prediction using Machine Learning. i-manager's Journal on Software Engineering, 15(3), 37-41. https://doi.org/10.26634/jse.15.3.18133

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* Department of Information Technology, Bhailalbhai and Bhikhabhai Institute of Technology, Vallabh Vidyanagar, Gujarat, India.

** Department of Electronics and Communication Engineering, G H Patel College of Engineering and Technology (GCET), Gujarat, India.

Debabrat Bharali* , Wahengbam Shibananda Singh , Kuldeep Nath, Afrin Haque , Dipu Medhi

Ch. Sekhar* , K. Venkata Rao , M. H. M. Krishna Prasad *

, Department of Computer Science and Engineering, JNTU Kakinada, Andhra Pradesh, India.

Department of Computer Science and Engineering, Vignan's IIT Kakinada, Andhra Pradesh, India.

Preethi S.* , Priyadharshini K. S. , Kavitha A. *