Identifying Cancer Characteristics Utilizing Handwriting Method

Abstract

Handwriting is an action administered by the brain like each and every other action. This procedure is frequently insensible and is closely tied to instincts from brain. Any kind of sickness affects the kinetic movement and reflects in a person’s handwriting. To recognize the health and mental problems, it is important to focus on how the person writes instead of what person writes. This also makes the procedure of handwriting analysis is independent of at all languages. Person handwriting is scientific proof that whatsoever person writes subconsciously it affects in handwriting. The structures related to motion, time and pressure have been used for analysis of person health. Cancer is the second top cause of death globally, and is accountable for an estimated 9.8 million deaths in 2019. Universally, around 1 in 6 deaths is due to cancer. On an approximation 72% of deaths due to cancer are in middle and low salaried countries. One third deaths from cancer are due to 5 foremost dietary and behavioural risks that are low fruit and vegetable intake, lack of physical activity, high body mass index, tobacco use, and consumption of alcohol. Cancer can be cured if the person gets to know as soon as possible. So, substitute method to patterned whether the person is diagnosed from a cancer or not, can be done by handwriting sample. For this testing 100 various person sample are used for diverse handwriting data samples. To find a solution to this mounting problem we propose the method of cancer characteristics detection by utilizing handwritten text by machine learning, SVM. Various machine learning methods were used to find a model, which can discriminate statistically Cancer patients with approximately 90%accuracy. The classification we use to discriminate are SVM, Naïve Bayes algorithms.