Performance Evaluation of Machine Learning Based Classifier Techniques In Prostate Cancer Prediction using Novel Decision Tree and Naive Bayes Classification Techniques

350 International Journal of Advance and Applied Research www.ijaar.co.in ISSN – 2347-7075 Impact Factor – 8.141 Peer Reviewed Bi-Monthly Vol. 6 No. 38 September - October - 2025 Performance Evaluation of Machine Learning Based Classifier Techniques In Prostate Cancer Prediction using Novel Decision Tree and Naive Bayes Classification Techniques Ms. Radhika Bhiste Computer Science Department, Dr. D Y Patil Arts, Commerce and Science College Akurdi, Pune 411044, INDIA Corresponding Author – Ms. Radhika Bhiste DOI - 10.5281/zenodo.17317444 Abstract: Prostate cancer is the most diagnosed malignancy worldwide and the sixth leading cause of cancer-related death in men. Diagnosis is primarily based on prostate-specific antigen testing, magnetic resonance imaging scans, and prostate tissue biopsies, although prostate-specific antigen testing for screening remains controversial. New diagnostic technologies are now available, including risk stratification bioassay tests, germline testing, and various positron emission tomography scans. When confined to the prostate, the disease is considered localized and potentially curable. If the disease has spread outside the prostate, bisphosphonates, rank ligand inhibitors, hormonal treatment, chemotherapy, radiopharmaceuticals, immunotherapy, focused radiation, and other targeted therapies can be used. This activity provides a comprehensive review of the current evaluation and management of prostate cancer, highlighting the role of the interprofessional team in improving care for affected patients. The proposed paper discussed ML based classifier techniques for prediction of affected prostate cancer glands. The Decision tree and naïve Bayes classifier are implemented for the prostate cancer patient data. Comparative analysis with the help of classification metrics is presented. The prediction accuracy using decision trees 82% and 79% for Naïve Baye classifier. Keywords: Deep Learning, Patient Management, Image Segmentation, Oncology, Prostate cancer Introduction: An introduction of machine learning based classification algorithms in oncological diseases aims at building efficient and accurate predictive modelling tools. These tools will help not only to predict the risk level of severity of cancers but also help in clinical and radiation oncology management [11] [14]. Predictive tools designed using ML techniques provide an optimal method in therapeutic and cancer patient management. New incidences of male prostate cancer are significantly increasing across the globe. It ranks among the major cancer type found in men. The Population Based Cancer Registry (PBCRs) and Hospital Based Cancer Registry (HBCRs) which are aimed to keep records of all cancer incidences estimated that the prostate cancer incidences and deaths are increasing at a faster rate. The data published by International Agency on Cancer research (IARC) shows that 19292789 new cancer incidences of all types are reported in 2020 [1][3]. This data also shows that new 1414259 prostate cancer incidences reported which are more than reported in year 2018 [2] [3]. India reported significant rise in the number of cancer incidences in all regions of IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Ms. Radhika Bhiste 351 the country. A total of 13324413 new cancer incidences of all types of cancers are reported with 851678 deaths cases. Incidences of new prostate cancer patients also increase in major cities of an India. There are 34540 new Prostate cancer patients reported in 2020. These patients are more than that of bladder 21096, Thyroid 20432, Gallbladder 19570 and 12642 Pancreas cancer patients [6]. Prostate cancer is a second leading cancer disease in Pune, Delhi, Thiruvananthapuram and Kolkata and third in cities like Mumbai and Bangalore [5]. Prostate cancer ranks 12th in all cancer types found in an India [6]. Prostate Gland Anatomy and Prostate Cancer: Reproductive system of male has seminal vesicles, Bulbourethral and Prostate accessory glands. These glands are responsible for transportation of semen and production of structural protein and alkaline solution that are needed in the formation of spermatophore [7]. Prostate gland size changes with an increase in men age. The prostatitis condition in prostate gland occurs by infection. When gland cell size changes abruptly, prostate cancer begins to form. This cancer spreads very slowly and starts causing symptoms for years and year. Fig.1. Normal and Cancer Prostate Gland Digital rectum examination [10], prostate specific antigen [9] Imaging test and prostate biopsy tests are used for possible cancer symptoms examination and diagnosis in male patients. Traditional prostate cancer testing tools sometimes become less accurate and digital rectum analysis and biopsy may cause infection, bleeding and pain due to external instrument use. The new approach has been introduced in cancer diagnosis using advanced radiographic tests and machine learning techniques. With the incursion of artificial intelligence techniques in spine related surgery, new approach has been adopted to create clinical analytics tools and diagnostic instruments. ML and AI techniques make clinical predictions and decision-making tasks easier [12] [13]. These techniques also play an important role in transforming medical imaging which incorporates medical radiology. Newly designed diagnostic imaging systems are utilizing the capabilities of AI and ML techniques. Specially designed Algorithms help performance tuning for imaging systems [4-9] and facilitate to quantify and detect various possible clinical scenarios.AI techniques not only show impressive performances in accurately identifying the cancer region in prostate gland in male reproductive system. Machine Learning In Prostate Cancer Detection: Researchers with the help of latest machine learning techniques are developing advance methods for detection and analyzing prostate cancer [24]. ML techniques can be best suited with imaging tests that are used for prostate cancer diagnosis. Radiography such as CT and MRI are the x-ray imaging testing methods that easily defines all prostate gland anatomical details [23]. This method provides details IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Ms. Radhika Bhiste 352 about lesion location, structural tissue changes in gland and size of the prostate gland. Conversion of data samples into its statistical probabilities, sigmoid functions are used in logistic regression given by A. Decision tree Classifier:  Selection of node root  Finding the key attribute from the given data using function Attribute selection Measure (ASM)  Dividing the main node into subset  Generate decision tree node  Repeat step of main node division B. Naïve Bayes Classifier:  Conversion of given data into frequency table  Probabilities calculation from given data samples  Bayes theorems calculate the posterior probabilities Proposed Work: An aim of the proposed paper is to implement the four ML based classifiers for prediction of the prostate cancer type from the given prostate cancer patient data. The cancer data is CSV based file that has information about prostate cancer results that are either malignant or benign and geometric information about cancer prostate gland. Decision tree and Naïve Bayes classifiers are implemented to predict the results based on the information provided in the given data. Following essential steps performed for the prediction process using decision Tree and Naïve Bayes Classifier techniques  Load the dataset  Feature extraction from the given dataset  Data collection for training and testing purposes  Train the model on the training data  Prediction on the basis of testing data Observations: The values of classification metrics are calculated by implementing the proposed classifiers for the prostate cancer prediction. The prostate cancer result is either Benign or Malignant. The benign prostate cancer is represented by 0 and malignant by 0. The values of the metrics for each classifier are given in respective tables. Table 1: Classification Metrics for Decision Tree Classifier Accuracy Precision Recall F1-Score 82% +Ve -Ve Sensitivity Specificity +Ve -Ve 0.75 0.92 0.94 0.74 0.83 0.80 Fig 2 Confusion Matrix for Decision Tree Classifier IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Ms. Radhika Bhiste 353 Table 2: Classification Metrics for Naïve bayes Classifier Accuracy Precision Recall F1-Score 79% +Ve -Ve Sensitivity Specificity +Ve -Ve 0.89 0.67 0.83 0.76 0.82 0.74 Fig 3 Confusion Matrix for Naïve Bayes Classifier Conclusion: The comparative analysis between Decision Tree and Naïve Bayes based ML classifiers is presented. The numerical data related to prostate cancer gland is used for this comparative analysis. The accuracy for Decision tree classifier (DT), KNN and Naive Bayes (NB) are 82%, 73% respectively. The ability to capture all positive samples called True Positive Rate (TPR) or sensitivity for given classifiers DT and NB are73% and 83% respectively. Thus, the prostate cancer prediction can be efficiently performed by using decision tree technique. Acknowledgment: I would like to express my gratitude to Dr. Mohan Waman Principal, Dr. D Y Patil Arts, Commerce and Science College Akurdi Pune for valuable guidance. References: 1. Hyuna Sung, Jacques Ferlay, Rebecca L. Siegel, Mathieu Laversanne, Isabelle Soerjomataram, Ahmedin Jemal; ―Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries‖; CA: A Cancer Journal for Clinicians, Volume 71, Issue 3, 04 February 2021 2. F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, A. Jemal; ―Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries‖; CA: A Cancer Journal for Clinicians, Volume 70, Issue 4, 06 April 2020 3. International Agency for Research on Cancer and Global Cancer Observatory; https://gco.iarc.fr/ 4. Krishnamoorthy Hariharan, Venugopal Padmanabha; ―Demographic and Disease characteristics of prostate cancer in India‖; Indian Journal of urology, Volume 32, Issue 2, Pages: 103-108, 2016 5. Prameesha Pereraa, Ayam Guptac, Nidhi Shuklab, Mamta Nehrac, Mukesh Sharmad, Sneha Mishrad IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Ms. Radhika Bhiste 354 ,Vikram Singh Chauhand, et al; ― Revisiting Prostate Cancer in India‖; Preprints , 26 November 2018 6. Prashant Mathur, Krishnan Sathishkumar, Meesha Chaturvedi, Priyanka Das, et al; ―Cancer Statistics, 2020: Report From National Cancer Registry Programme, India‖; Global Oncology, American Society of Clinical Oncology, 16 July 2020 7. Omesh Singh, Srinivasa Rao Bolla; ―Anatomy, Abdomen and Pelvis, Prostate‖; StatPearls Publishing, January 2021 8. Anil Bhavsar, Sadhna Verma; ―Multiparametric MRI in Prostate Cancer‖; BioMed Research International, vol. 2014, Article ID 728539, 9 pages, 2014 9. Michael Obana, Henry O'Lawrence; ―Prostate Cancer Screening: PSA Test awareness among adult males‖; J Health Hum Serv Adm. 2015 10. Leen Naji, Harkanwal Randhawa, Zahra Sohani; ―Digital Rectal Examination for Prostate Cancer Screening in Primary Care: A Systematic Review and MetaAnalysis‖; Analysis of Family Medicine, 149-154, March 11. Mohammed AliKazem; ―Predictive models in cancer management: A guide for clinicians‖; Elsevier, The Surgeon, Volume 15, Issue 2, Pages 93-97, April 2017 12. Vincenzo Valentini, Nicola Dinapoli, Andrea Damiani; ―The future of predictive models in radiation oncology: from extensive data mining to reliable modeling of the results‖; Future Oncology, March 2013 13. Derek J Van Booven, Manish Kuchakulla, Raghav Pai, Fabio S Frech, Reshna Ramasahayam, Pritika Reddy, Madhumita Parmar, Ranjith Ramasamy, Himanshu Arora1; ―A Systematic Review of Artificial Intelligence in Prostate Cancer‖; Dovepress Journal, Research and Reports in Urology, 2021 14. Ronan Thenault, Kevin Kaulanjan,Thomas Darde, Nathalie Rioux Leclercq, Karim Bensalah, Marie Mermier, Zine-eddine Khene, Benoit Peyronnet, Shahrokh Shariat, Benjamin Pradere , Romain Mathieu; ―The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review‖; Mdpi, Applied Science, 2020 15. Lidia Alcala Mata , Juan Antonio Retamero, Rajan T. Gupta, Roberto García Figueras, Antonio Luna; ―Artificial Intelligence–assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation‖; RadioGraphics, Vo1ume 41, Number 6, 1 October 2021 16. E. Checcucci, S. Rosati, S. De Cillis, et al; ― Artificial intelligence for target prostate biopsy outcomes prediction the potential application of fuzzy logic‖; Prostate Cancer and Prostatic Diseases, 3 September 2021 17. Tobias Penzkofer, Anwar R. Padhani, Baris Turkbey, Masoom A. Haider, Henkjan Huisman, Jochen Walz, Georg Salomon, Ivo G. Schoots, Jonathan Richenberg, Geert Villeirs, Valeria Panebianco, Olivier Rouviere, Vibeke Berg Logager, Jelle Barentsz; ―ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer IJAAR Vol. 6 No. 38 ISSN – 2347-7075 Ms. Radhika Bhiste 355 using Magnetic Resonance Imaging‖; SpringerLink, European Radiology, 31, 9567-9578, 15 May 2021 18. B. M. Zeeshan Hameed, Milap Shah, Nithesh Naik, Sufyan Ibrahim, Bhaskar Somani, Patrick Rice, Naeem Soomro, Bhavan Prasad Rai; ―Contemporary application of artificial intelligence in prostate cancer : an i-TRUE study‖; SAGE Journal, Therapeutic Advances in Urology, Volume 31, 23 January 2021 19. Chalida Aphinives, Potchavit Aphinives; ―Artificial intelligence development for detecting prostate cancer in MRI‖; Springer Open, Egyptian Journal of Radiology and Nuclear Medicine, Volume 52, Article number 87, 26 March 2021 20. Ellery Wulczyn, Kunal Nagpal, Matthew Symonds, Melissa Moran, Markus Plass, et al; ―Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading‖; Communication Medicine, Volume 1, Article number 10, 30 June 2021 21. Konstantina Kourou, Konstantina P. Exarchos, Costas Papaloukas, Prodomos Sakaloglou; ―Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis‖; Elsevier, Computational and Structural Biotechnology Journal, Volume 19, Pages 5546-5555, 2021 22. Jasper J. Twilt , Kicky G. van Leeuwen , Henkjan J. Huisman , Jurgen J. Fütterer and Maarten de Rooij; ―Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review‖; Mdpi, Diagnostic, 11, 959, 26 May 2021 23. Eirini Polymeri, Henrik KjolhedeIcon, Olof Enqvist, Johannes Ulen, Mads H. Poulseng , et al; ―Artificial intelligence-based measurements of PET/CT imaging biomarkers are associated with disease-specific survival of high-risk prostate cancer patients‖; Scandinavian Journal of Urology, 25 September 2021 24. Michela Antonelli, Edward W. Johnston, Nikolaos Dikaios; ―Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists‖; European radiology, 2019 25. Jill S Barnholtz-Sloan, Xiaowei Guan, Charnita Zeigler-Johnson, Neal J Meropol, Timothy R Rebbeck; ―Decision tree-based modeling of androgen pathway genes and prostate cancer risk‖; Cancer Epidemiol Biomarkers Prev., June 2011 26. Li Zhang, Longchao Li, Min Tang, Yi Huan, Xiaoling, Zhang, Xia Zhe; ―A new approach to diagnosing prostate cancer through magnetic resonance imaging‖; Alexandria Engineering Journal, Volume 60, Issue 1, Pages 897-904, February 2021