BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Canada/Eastern BEGIN:DAYLIGHT DTSTART:20220313T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20221106T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20220507T022208Z UID:5C4CBCE2-00B0-4BB0-A363-0738263DFA18 DTSTART;TZID=Canada/Eastern:20220506T180000 DTEND;TZID=Canada/Eastern:20220506T190000 DESCRIPTION:Cancer ranks as a leading cause of death and an important barri er to increasing life expectancy everywhere. According to available data\, lung cancer contributes the most to cancer deaths. Also\, according to av ailable data\, those diagnosed early have a 50 percent chance of survival over those diagnosed with late-stage cancer. It means that early detection is paramount to the survival of a lung cancer patient\, leading to a redu ction in the number of cancer deaths. We\, therefore\, evaluated six diffe rent machine learning algorithms to see which one performed optimally in a ccurately predicting the level of lung cancer development in a patient. We considered various parameters when choosing the dataset for this evaluati on as the pathogenesis of lung cancer involves a combination of intrinsic factors and exposure to environmental carcinogens. We also considered vary ing the features in our data\, categorizing them under diagnostic risk fac tors (age\, gender\, alcohol use\, air pollution\, balanced diet\, obesity \, smoking\, passive smoker) and symptoms (fatigue\, weight loss\, shortne ss of breath\, swallowing difficulty\, frequent cold\, dry cough) and the inferences we drew from this indicated that those that have the symptom fe atures prior to diagnosis had the highest chance of being diagnosed with a high level of cancer. The final results of our evaluation showed that the best levels of predictions on new data were achieved by optimized Random Forest\, KNN\, and SVM models.\n\nSpeaker(s): Rakesh Pattanayak\, \n\nToro nto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312340 LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.o rg/m/312340 ORGANIZER:reza.dibaj@ieee.org SEQUENCE:1 SUMMARY:Cancer Level Detection System – Students Research in ML and DL at Durham College URL;VALUE=URI:https://events.vtools.ieee.org/m/312340 X-ALT-DESC:Description: <br /><p>Cancer ranks as a leading cause of death a nd an important barrier to increasing life expectancy everywhere. Accordin g to available data\, lung cancer contributes the most to cancer deaths. A lso\, according to available data\, those diagnosed early have a 50 percen t chance of survival over those diagnosed with late-stage cancer. It means that early detection is paramount to the survival of a lung cancer patien t\, leading to a reduction in the number of cancer deaths. We\, therefore\ , evaluated six different machine learning algorithms to see which one per formed optimally in accurately predicting the level of lung cancer develop ment in a patient. We considered various parameters when choosing the data set for this evaluation as the pathogenesis of lung cancer involves a comb ination of intrinsic factors and exposure to environmental carcinogens. We also considered varying the features in our data\, categorizing them unde r diagnostic risk factors (age\, gender\, alcohol use\, air pollution\, ba lanced diet\, obesity\, smoking\, passive smoker) and symptoms (fatigue\, weight loss\, shortness of breath\, swallowing difficulty\, frequent cold\ , dry cough) and the inferences we drew from this indicated that those tha t have the symptom features prior to diagnosis had the highest chance of b eing diagnosed with a high level of cancer. The final results of our evalu ation showed that the best levels of predictions on new data were achieved by optimized Random Forest\, KNN\, and SVM models.</p> END:VEVENT END:VCALENDAR